Twitter API

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package stream provides methods for listening to Twitter's Streaming APIs. To begin using you will need credentials for an app setup in Twitter. Then you can get your timeline like so, Warning, this library does not do any error handling at the moment, it ignores any incoming messages that do not look like tweets.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

A fully OAuth-authenticated implementation of Twitter's REST API v1.1 See https:dev.twitter.com/docs/api/ Usage example: Twitter uses OAUTH 1.0a for authentication and tweetlib supports the 3-legged authorization method. See https://dev.twitter.com/docs/auth/3-legged-authorization 1. Setup a Config structure with the Consuler Key and Secret found on your application's page. You can create new applications by visiting http://dev.twitter.com/apps/new. Twitter also requires a callback URL that will receive Twitter's token 2. Populate a tweetlibTransport structure with the Config from the previous step and, for now, an empty Token 2a. (Optional) tweetlib.Transport uses the http package to talk to Twitter by default. This may not be possible or desirable. For example, if the Client is to be used in a Google Appengine app, it becomes necessary to change the underlying transport to be used. E.g.: 3. Not it's possible to request the temporary token. This will start the little Oauth dance with Twitter 4. With the tweetlib.TempToken ready, now it's time to request the user to authorize your application. This is done by redirecting the user to the URL returned by tweetlib.TempToken.AuthURL(). (Note that you must save the temporary token as it will be necessary later to request the permanent token) 5. The user will be promted by Twitter to authorize your application. If they authorize it, Twitter will call your callback as set in step 1. Twitter will issue a GET request to your callback with two parameters: 6. Finally, you'll request the permanent token from Twitter Note that you do not need to update your tweetlib.Transport.Token with the new token, as this is done automatically, meaning you can immediatly start making API calls with the same transport. That said, you must save the token for future use so you don't have to go through all this dance again each time. Next time you need to make calls on behalf of a user you already have a token for, you simply set the Transport with the saved token. Using the Twitter API we can obtain an authentication token for only our application Yes it's that easy. Now you have a token that you can use to make API calls We can invalidate a previously obtained application only token by passing it to the InvalidateToken function For making calls based off a users account, making an API call is trivial once authentication is set up. It all starts with getting an API Client object: For making calls for applications only, we can use our previously obtained authentication token and pass it to the application-only client constructor. Unlike utilizing user authenticated APIs, we do not need to use any custom instances of http.Client Once you have the client, you can make API calls easily. For example, to post a tweet as the authenticating user The vast majority of API calls to the Twitter REST API takes one or two required parameters along with any number of optional ones. For example, when updating the status, it is possible to attached geographical coordinates to it via the 'lat' and 'long' optional parameters. To provide optional parameters, use tweetlib.Optionals There's also two ways of making arbitrary API calls. This is useful when you need to call a new API that is not directly supported by tweetlib's utility functions or maybe you want better control of the response objects. The first way is using Client.Call like this: Client.Call will try to unmarshal the response returned from Twitter. If however you wish to do it yourself or maybe not use the types defined by tweetlib (User, Tweet, etc), you can use CallJSON instead: These two functions are usually internally by the many helper functions defined in tweetlib and also add flexibility to tweetlib - A fully oauth-authenticated Go Twitter library Copyright 2011 The Tweetlib Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

A fully OAuth-authenticated implementation of Twitter's REST API v1.1 See https:dev.twitter.com/docs/api/ Usage example: Twitter uses OAUTH 1.0a for authentication and tweetlib supports the 3-legged authorization method. See https://dev.twitter.com/docs/auth/3-legged-authorization 1. Setup a Config structure with the Consuler Key and Secret found on your application's page. You can create new applications by visiting http://dev.twitter.com/apps/new. Twitter also requires a callback URL that will receive Twitter's token 2. Populate a tweetlibTransport structure with the Config from the previous step and, for now, an empty Token 2a. (Optional) tweetlib.Transport uses the http package to talk to Twitter by default. This may not be possible or desirable. For example, if the Client is to be used in a Google Appengine app, it becomes necessary to change the underlying transport to be used. E.g.: 3. Not it's possible to request the temporary token. This will start the little Oauth dance with Twitter 4. With the tweetlib.TempToken ready, now it's time to request the user to authorize your application. This is done by redirecting the user to the URL returned by tweetlib.TempToken.AuthURL(). (Note that you must save the temporary token as it will be necessary later to request the permanent token) 5. The user will be promted by Twitter to authorize your application. If they authorize it, Twitter will call your callback as set in step 1. Twitter will issue a GET request to your callback with two parameters: 6. Finally, you'll request the permanent token from Twitter Note that you do not need to update your tweetlib.Transport.Token with the new token, as this is done automatically, meaning you can immediatly start making API calls with the same transport. That said, you must save the token for future use so you don't have to go through all this dance again each time. Next time you need to make calls on behalf of a user you already have a token for, you simply set the Transport with the saved token. Using the Twitter API we can obtain an authentication token for only our application Yes it's that easy. Now you have a token that you can use to make API calls We can invalidate a previously obtained application only token by passing it to the InvalidateToken function For making calls based off a users account, making an API call is trivial once authentication is set up. It all starts with getting an API Client object: For making calls for applications only, we can use our previously obtained authentication token and pass it to the application-only client constructor. Unlike utilizing user authenticated APIs, we do not need to use any custom instances of http.Client Once you have the client, you can make API calls easily. For example, to post a tweet as the authenticating user The vast majority of API calls to the Twitter REST API takes one or two required parameters along with any number of optional ones. For example, when updating the status, it is possible to attached geographical coordinates to it via the 'lat' and 'long' optional parameters. To provide optional parameters, use tweetlib.Optionals There's also two ways of making arbitrary API calls. This is useful when you need to call a new API that is not directly supported by tweetlib's utility functions or maybe you want better control of the response objects. The first way is using Client.Call like this: Client.Call will try to unmarshal the response returned from Twitter. If however you wish to do it yourself or maybe not use the types defined by tweetlib (User, Tweet, etc), you can use CallJSON instead: These two functions are usually internally by the many helper functions defined in tweetlib and also add flexibility to tweetlib - A fully oauth-authenticated Go Twitter library Copyright 2011 The Tweetlib Authors. All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package lingua accurately detects the natural language of written text, be it long or short. Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. So far, the only other comprehensive open source library in the Go ecosystem for this task is Whatlanggo (https://github.com/abadojack/whatlanggo). Unfortunately, it has two major drawbacks: 1. Detection only works with quite lengthy text fragments. For very short text snippets such as Twitter messages, it does not provide adequate results. 2. The more languages take part in the decision process, the less accurate are the detection results. Lingua aims at eliminating these problems. It nearly does not need any configuration and yields pretty accurate results on both long and short text, even on single words and phrases. It draws on both rule-based and statistical methods but does not use any dictionaries of words. It does not need a connection to any external API or service either. Once the library has been downloaded, it can be used completely offline. Compared to other language detection libraries, Lingua's focus is on quality over quantity, that is, getting detection right for a small set of languages first before adding new ones. Currently, 75 languages are supported. They are listed as variants of type Language. Lingua is able to report accuracy statistics for some bundled test data available for each supported language. The test data for each language is split into three parts: 1. a list of single words with a minimum length of 5 characters 2. a list of word pairs with a minimum length of 10 characters 3. a list of complete grammatical sentences of various lengths Both the language models and the test data have been created from separate documents of the Wortschatz corpora (https://wortschatz.uni-leipzig.de) offered by Leipzig University, Germany. Data crawled from various news websites have been used for training, each corpus comprising one million sentences. For testing, corpora made of arbitrarily chosen websites have been used, each comprising ten thousand sentences. From each test corpus, a random unsorted subset of 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively. Given the generated test data, I have compared the detection results of Lingua, and Whatlanggo running over the data of Lingua's supported 75 languages. Additionally, I have added Google's CLD3 (https://github.com/google/cld3/) to the comparison with the help of the gocld3 bindings (https://github.com/jmhodges/gocld3). Languages that are not supported by CLD3 or Whatlanggo are simply ignored during the detection process. Lingua clearly outperforms its contenders. Every language detector uses a probabilistic n-gram (https://en.wikipedia.org/wiki/N-gram) model trained on the character distribution in some training corpus. Most libraries only use n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text fragments consisting of multiple sentences. For short phrases or single words, however, trigrams are not enough. The shorter the input text is, the less n-grams are available. The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct language. A second important difference is that Lingua does not only use such a statistical model, but also a rule-based engine. This engine first determines the alphabet of the input text and searches for characters which are unique in one or more languages. If exactly one language can be reliably chosen this way, the statistical model is not necessary anymore. In any case, the rule-based engine filters out languages that do not satisfy the conditions of the input text. Only then, in a second step, the probabilistic n-gram model is taken into consideration. This makes sense because loading less language models means less memory consumption and better runtime performance. In general, it is always a good idea to restrict the set of languages to be considered in the classification process using the respective api methods. If you know beforehand that certain languages are never to occur in an input text, do not let those take part in the classifcation process. The filtering mechanism of the rule-based engine is quite good, however, filtering based on your own knowledge of the input text is always preferable. There might be classification tasks where you know beforehand that your language data is definitely not written in Latin, for instance. The detection accuracy can become better in such cases if you exclude certain languages from the decision process or just explicitly include relevant languages. Knowing about the most likely language is nice but how reliable is the computed likelihood? And how less likely are the other examined languages in comparison to the most likely one? In the example below, a slice of ConfidenceValue is returned containing those languages which the calling instance of LanguageDetector has been built from. The entries are sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 0.0. By default, Lingua uses lazy-loading to load only those language models on demand which are considered relevant by the rule-based filter engine. For web services, for instance, it is rather beneficial to preload all language models into memory to avoid unexpected latency while waiting for the service response. If you want to enable the eager-loading mode, you can do it as seen below. Multiple instances of LanguageDetector share the same language models in memory which are accessed asynchronously by the instances. By default, Lingua returns the most likely language for a given input text. However, there are certain words that are spelled the same in more than one language. The word `prologue`, for instance, is both a valid English and French word. Lingua would output either English or French which might be wrong in the given context. For cases like that, it is possible to specify a minimum relative distance that the logarithmized and summed up probabilities for each possible language have to satisfy. It can be stated as seen below. Be aware that the distance between the language probabilities is dependent on the length of the input text. The longer the input text, the larger the distance between the languages. So if you want to classify very short text phrases, do not set the minimum relative distance too high. Otherwise Unknown will be returned most of the time as in the example below. This is the return value for cases where language detection is not reliably possible.

Package main is used and deployed as a Google cloud function. It is triggered by any changes to the /humans collection in Firestore. It then calls the Twitter API to follow/unfollow humans.

Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

Package hashtag implements extraction of Twitter type hashtags, mentions and replies form text in Go. This package partially ports extraction routines from Twitter's official Java package at https://github.com/twitter/twitter-text to Go and runs most of the standard twitter-text conformance tests. It does not yet implement URL extraction (and hence URL/Hashtag overlaps), cashtags and lists Since the package attempts to closely follow the Twitter-Text Java API, function names may be longer than typical Go package function names

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package lingua accurately detects the natural language of written text, be it long or short. Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. So far, the only other comprehensive open source library in the Go ecosystem for this task is Whatlanggo (https://github.com/abadojack/whatlanggo). Unfortunately, it has two major drawbacks: 1. Detection only works with quite lengthy text fragments. For very short text snippets such as Twitter messages, it does not provide adequate results. 2. The more languages take part in the decision process, the less accurate are the detection results. Lingua aims at eliminating these problems. It nearly does not need any configuration and yields pretty accurate results on both long and short text, even on single words and phrases. It draws on both rule-based and statistical methods but does not use any dictionaries of words. It does not need a connection to any external API or service either. Once the library has been downloaded, it can be used completely offline. Compared to other language detection libraries, Lingua's focus is on quality over quantity, that is, getting detection right for a small set of languages first before adding new ones. Currently, 75 languages are supported. They are listed as variants of type Language. Lingua is able to report accuracy statistics for some bundled test data available for each supported language. The test data for each language is split into three parts: 1. a list of single words with a minimum length of 5 characters 2. a list of word pairs with a minimum length of 10 characters 3. a list of complete grammatical sentences of various lengths Both the language models and the test data have been created from separate documents of the Wortschatz corpora (https://wortschatz.uni-leipzig.de) offered by Leipzig University, Germany. Data crawled from various news websites have been used for training, each corpus comprising one million sentences. For testing, corpora made of arbitrarily chosen websites have been used, each comprising ten thousand sentences. From each test corpus, a random unsorted subset of 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively. Given the generated test data, I have compared the detection results of Lingua, and Whatlanggo running over the data of Lingua's supported 75 languages. Additionally, I have added Google's CLD3 (https://github.com/google/cld3/) to the comparison with the help of the gocld3 bindings (https://github.com/jmhodges/gocld3). Languages that are not supported by CLD3 or Whatlanggo are simply ignored during the detection process. Lingua clearly outperforms its contenders. Every language detector uses a probabilistic n-gram (https://en.wikipedia.org/wiki/N-gram) model trained on the character distribution in some training corpus. Most libraries only use n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text fragments consisting of multiple sentences. For short phrases or single words, however, trigrams are not enough. The shorter the input text is, the less n-grams are available. The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct language. A second important difference is that Lingua does not only use such a statistical model, but also a rule-based engine. This engine first determines the alphabet of the input text and searches for characters which are unique in one or more languages. If exactly one language can be reliably chosen this way, the statistical model is not necessary anymore. In any case, the rule-based engine filters out languages that do not satisfy the conditions of the input text. Only then, in a second step, the probabilistic n-gram model is taken into consideration. This makes sense because loading less language models means less memory consumption and better runtime performance. In general, it is always a good idea to restrict the set of languages to be considered in the classification process using the respective api methods. If you know beforehand that certain languages are never to occur in an input text, do not let those take part in the classifcation process. The filtering mechanism of the rule-based engine is quite good, however, filtering based on your own knowledge of the input text is always preferable. There might be classification tasks where you know beforehand that your language data is definitely not written in Latin, for instance. The detection accuracy can become better in such cases if you exclude certain languages from the decision process or just explicitly include relevant languages. Knowing about the most likely language is nice but how reliable is the computed likelihood? And how less likely are the other examined languages in comparison to the most likely one? In the example below, a slice of ConfidenceValue is returned containing those languages which the calling instance of LanguageDetector has been built from. The entries are sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 0.0. By default, Lingua uses lazy-loading to load only those language models on demand which are considered relevant by the rule-based filter engine. For web services, for instance, it is rather beneficial to preload all language models into memory to avoid unexpected latency while waiting for the service response. If you want to enable the eager-loading mode, you can do it as seen below. Multiple instances of LanguageDetector share the same language models in memory which are accessed asynchronously by the instances. By default, Lingua returns the most likely language for a given input text. However, there are certain words that are spelled the same in more than one language. The word `prologue`, for instance, is both a valid English and French word. Lingua would output either English or French which might be wrong in the given context. For cases like that, it is possible to specify a minimum relative distance that the logarithmized and summed up probabilities for each possible language have to satisfy. It can be stated as seen below. Be aware that the distance between the language probabilities is dependent on the length of the input text. The longer the input text, the larger the distance between the languages. So if you want to classify very short text phrases, do not set the minimum relative distance too high. Otherwise Unknown will be returned most of the time as in the example below. This is the return value for cases where language detection is not reliably possible.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.

Package lingua accurately detects the natural language of written text, be it long or short. Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. So far, the only other comprehensive open source library in the Go ecosystem for this task is Whatlanggo (https://github.com/abadojack/whatlanggo). Unfortunately, it has two major drawbacks: 1. Detection only works with quite lengthy text fragments. For very short text snippets such as Twitter messages, it does not provide adequate results. 2. The more languages take part in the decision process, the less accurate are the detection results. Lingua aims at eliminating these problems. It nearly does not need any configuration and yields pretty accurate results on both long and short text, even on single words and phrases. It draws on both rule-based and statistical methods but does not use any dictionaries of words. It does not need a connection to any external API or service either. Once the library has been downloaded, it can be used completely offline. Compared to other language detection libraries, Lingua's focus is on quality over quantity, that is, getting detection right for a small set of languages first before adding new ones. Currently, 75 languages are supported. They are listed as variants of type Language. Lingua is able to report accuracy statistics for some bundled test data available for each supported language. The test data for each language is split into three parts: 1. a list of single words with a minimum length of 5 characters 2. a list of word pairs with a minimum length of 10 characters 3. a list of complete grammatical sentences of various lengths Both the language models and the test data have been created from separate documents of the Wortschatz corpora (https://wortschatz.uni-leipzig.de) offered by Leipzig University, Germany. Data crawled from various news websites have been used for training, each corpus comprising one million sentences. For testing, corpora made of arbitrarily chosen websites have been used, each comprising ten thousand sentences. From each test corpus, a random unsorted subset of 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively. Given the generated test data, I have compared the detection results of Lingua, and Whatlanggo running over the data of Lingua's supported 75 languages. Additionally, I have added Google's CLD3 (https://github.com/google/cld3/) to the comparison with the help of the gocld3 bindings (https://github.com/jmhodges/gocld3). Languages that are not supported by CLD3 or Whatlanggo are simply ignored during the detection process. Lingua clearly outperforms its contenders. Every language detector uses a probabilistic n-gram (https://en.wikipedia.org/wiki/N-gram) model trained on the character distribution in some training corpus. Most libraries only use n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text fragments consisting of multiple sentences. For short phrases or single words, however, trigrams are not enough. The shorter the input text is, the less n-grams are available. The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct language. A second important difference is that Lingua does not only use such a statistical model, but also a rule-based engine. This engine first determines the alphabet of the input text and searches for characters which are unique in one or more languages. If exactly one language can be reliably chosen this way, the statistical model is not necessary anymore. In any case, the rule-based engine filters out languages that do not satisfy the conditions of the input text. Only then, in a second step, the probabilistic n-gram model is taken into consideration. This makes sense because loading less language models means less memory consumption and better runtime performance. In general, it is always a good idea to restrict the set of languages to be considered in the classification process using the respective api methods. If you know beforehand that certain languages are never to occur in an input text, do not let those take part in the classifcation process. The filtering mechanism of the rule-based engine is quite good, however, filtering based on your own knowledge of the input text is always preferable. There might be classification tasks where you know beforehand that your language data is definitely not written in Latin, for instance. The detection accuracy can become better in such cases if you exclude certain languages from the decision process or just explicitly include relevant languages. Knowing about the most likely language is nice but how reliable is the computed likelihood? And how less likely are the other examined languages in comparison to the most likely one? In the example below, a slice of ConfidenceValue is returned containing those languages which the calling instance of LanguageDetector has been built from. The entries are sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 0.0. By default, Lingua uses lazy-loading to load only those language models on demand which are considered relevant by the rule-based filter engine. For web services, for instance, it is rather beneficial to preload all language models into memory to avoid unexpected latency while waiting for the service response. If you want to enable the eager-loading mode, you can do it as seen below. Multiple instances of LanguageDetector share the same language models in memory which are accessed asynchronously by the instances. By default, Lingua returns the most likely language for a given input text. However, there are certain words that are spelled the same in more than one language. The word `prologue`, for instance, is both a valid English and French word. Lingua would output either English or French which might be wrong in the given context. For cases like that, it is possible to specify a minimum relative distance that the logarithmized and summed up probabilities for each possible language have to satisfy. It can be stated as seen below. Be aware that the distance between the language probabilities is dependent on the length of the input text. The longer the input text, the larger the distance between the languages. So if you want to classify very short text phrases, do not set the minimum relative distance too high. Otherwise Unknown will be returned most of the time as in the example below. This is the return value for cases where language detection is not reliably possible.

Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

nolint Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

Package oauth1 is a Go implementation of the OAuth1 spec RFC 5849. It allows end-users to authorize a client (consumer) to access protected resources on their behalf (e.g. login) and allows clients to make signed and authorized requests on behalf of a user (e.g. API calls). It takes design cues from golang.org/x/oauth2, providing an http.Client which handles request signing and authorization. Package oauth1 implements the OAuth1 authorization flow and provides an http.Client which can sign and authorize OAuth1 requests. To implement "Login with X", use the https://github.com/dghubble/gologin packages which provide login handlers for OAuth1 and OAuth2 providers. To call the Twitter, Digits, or Tumblr OAuth1 APIs, use the higher level Go API clients. * https://github.com/dghubble/go-twitter * https://github.com/dghubble/go-digits * https://github.com/benfb/go-tumblr Perform the OAuth 1 authorization flow to ask a user to grant an application access to his/her resources via an access token. 1. When a user performs an action (e.g. "Login with X" button calls "/login" route) get an OAuth1 request token (temporary credentials). 2. Obtain authorization from the user by redirecting them to the OAuth1 provider's authorization URL to grant the application access. Receive the callback from the OAuth1 provider in a handler. 3. Acquire the access token (token credentials) which can later be used to make requests on behalf of the user. Check the examples to see this authorization flow in action from the command line, with Twitter PIN-based login and Tumblr login. Use an access Token to make authorized requests on behalf of a user. Check the examples to see Twitter and Tumblr requests in action.

Package anaconda provides structs and functions for accessing version 1.1 of the Twitter API. Successful API queries return native Go structs that can be used immediately, with no need for type assertions. If you already have the access token (and secret) for your user (Twitter provides this for your own account on the developer portal), creating the client is simple: Executing queries on an authenticated TwitterApi struct is simple. Certain endpoints allow separate optional parameter; if desired, these can be passed as the final parameter. Anaconda implements most of the endpoints defined in the Twitter API documentation: https://dev.twitter.com/docs/api/1.1. For clarity, in most cases, the function name is simply the name of the HTTP method and the endpoint (e.g., the endpoint `GET /friendships/incoming` is provided by the function `GetFriendshipsIncoming`). In a few cases, a shortened form has been chosen to make life easier (for example, retweeting is simply the function `Retweet`) More detailed information about the behavior of each particular endpoint can be found at the official Twitter API documentation.