alchemy_language

AlchemyLanguage

AlchemyLanguage is a collection of APIs that offer text analysis through natural language processing. This set of APIs can analyze text to help you understand its concepts, entities, keywords, sentiment, and more. Additionally, you can create a custom model for some APIs to get specific results that are tailored to your domain.

Ruby client library to quickly get started with the various Alchemy Language services.

Prerequisites

You authenticate to the AlchemyLanguage API by passing an API key as a query parameter in each call.

To get an API key, you'll need to sign up for IBM Bluemix. After you create an account:

Log in to Bluemix and go to the AlchemyAPI service page. Click the "Create" button. Click the "Service Credentials" button from the AlchemyAPI page in your Bluemix dashboard to view your API key.

After create bluemix account and create autheticate token of AlchemyLanguage, you can use another gem for env variables as (dotenv, figaro or which you want to create env variables).

Then in (.env) file you should add:

token=$your_token_from_bluemix

AlchemyLanguage will take your token and it will do request with this token, it's it!

Getting Started in ruby

You can install this library

gem install 'alchemy_language'

and use

require 'alchemy_language'

Getting Started in Rails

AlchemyLanguage works with Rails > 4.1 onwards. You can add it to your Gemfile with:

gem 'alchemy_language'

Then run bundle install

Use

Service

Alchemy Language provide three kind of service

• Url service
• Text service
• HTML service

Url Service

Initially create object with path when you want to do request

  url = "http://www.ibm.com/watson/"
  @alchemy_service = AlchemyLanguage::UrlService.new(url)

Text Service

Initially create object with text when you want to do request

  text = "some text"
  @alchemy_service = AlchemyLanguage::TextService.new(text)

HTML Service

Initially create object with path when you want to do request

  url = "http://www.ibm.com/watson/"
  @alchemy_service = AlchemyLanguage::HtmlService.new(text)

Models

All Services contain these models:

  .
  ├── Author
  ├── Concept
  ├── DateExtraxtion
  ├── EmotionAnalysis
  ├── TargetedEmotion
  ├── Entity
  ├── FeedDetection
  ├── Keyword
  ├── LanguageDetection
  ├── Microformat
  ├── PublicationDate
  ├── Relation
  ├── TypedRelation
  ├── SentimentAnalysis
  ├── TargetedSentiment
  ├── Taxonomy
  ├── Text
  ├── RawText
  ├── TitleExtraction
  ├── CombinedCall

  20 models

Authors

Authors - extracts author information from news articles or blog posts. Author extraction enables the categorization of content from online publications by author, and can be used with other AlchemyLanguage functions to generate tag clouds, identify sentiment towards topics and authors, and so on.

Services: [html, url]

Fetch all result of author model

  @alchemy_service.author.result

Concepts

Concepts - identifes concepts with which the input text is associated, based on other concepts and entities that are present in that text. Concept-related API functions understand how concepts relate, and can identify concepts that are not directly referenced in the text. For example, if an article mentions CERN and the Higgs boson, the Concepts API functions will identify Large Hadron Collider as a concept even if that term is not mentioned explicitly in the page. Concept tagging enables higher level analysis of input content than just basic keyword identification.

Services: [html, url, text]

Fetch all result of Concepts model

  @alchemy_service.concept(
    knowledgeGraph: 1
  ).result

Dates

Dates - extracts natural language date/time expressions from text, normalizes them to an ISO date string (such as "20040104T000000"), and identifies concepts with which those dates are associated.

Services: [html, url, text]

Fetch all result of Dates model

  @alchemy_service.date_extraction(
    anchorDate: "2016-03-22 00:00:00"
  ).result

Emotion Analysis

Emotion Analysis - detects anger, disgust, fear, joy, and sadness implied in English text. Emotion Analysis can detect emotions that are associated with targeted phrases, entities, or keywords, or it can analyze the overall emotional tone of your content.

Services: [html, url, text]

Fetch all result of Emotion Analysis model

  @alchemy_service.emotion_analysis(
    targets: "Target 1|Target 2"
  ).result

Targeted Emotion

Targeted Emotion searches your content for up to 20 targets, and uses context to analyze emotions associated with each phrase. You can analyze emotions for keywords and entities by passing an emotion=1 parameter when you use the respective API methods, or when you extract keywords and entities in a Combined Call.

Services: [html, url, text]

Fetch all result of Targeted Emotio model

  @alchemy_service.targeted_emotion.result

Entities

Entities - returns items such as persons, places, and organizations that are present in the input text. Entity extraction adds semantic knowledge to content to help understand the subject and context of the text that is being analyzed. The entity extraction technniques used by the AlchemyLanguage service are based on sophisticated statistical algorithms and natural language processing technology, and are unique in the industry with their support for multilingual analysis, context-sensitive disambiguation, and quotations extraction. You can specify a custom model in your request to identify a custom set of entity types in your content, enabling domain-specific entity extraction.

Services: [html, url, text]

Fetch all result of Entities model

  @alchemy_service.entity(
    maxRetrieve: = 3
  ).result

Feeds

Feeds - extracts any links to web feeds that are embedded in a web page. Web feeds are often embedded in websites to enable automated access to syndicated content. Any feeds that are detected can subsequently be used to discover new content, such as blog posts, news articles, and comment streams.

Services: [html, url]

Fetch all result of Feeds model

  @alchemy_service.feed_detection.result

Keywords

Keywords - important topics in your content that are typically used when indexing data, generating tag clouds, or when searching. The AlchemyLanguage service automatically identifies supported languages (see the next bullet) in your input content, and then identifies and ranks keywords in that content. Sentiment can also be associated with each keyword by using the AlchemyLanguage sentiment analysis capabilities.

Services: [html, url, text]

Fetch all result of Keywords model

  @alchemy_service.keyword.result

Language

Language - detects the natural language in which input text, HTML, or web-based content is written. Language identification functions can identify English, German, French, Italian, Portuguese, Russian, Spanish and Swedish. These functions enable applications to categorize or filter content based on the language in which it was written.

Services: [html, url, text]

Fetch all result of Language model

  @alchemy_service.language_detection.result

Microformats

Microformats - processes microformat information that is included in the HTML of some webpages to add semantic information and to enable easier scanning and processing of those pages by software. The information extracted from web pages by the Microformats method can be used for tasks such as webpage categorization and content discovery.

Services: [html, url]

Fetch all result of Microformats model

  @alchemy_service.microformat.result

Publication Date

Publication Date - extracts publication date information, if present, from web pages. The date on which a web page was published can be critical when analyzing trends based on text attributes, such as when tracking changing sentiment towards specific topics, products, or brands. The Publication Date API functions understand most date formats, can identify publication dates in different portions of a document, and can differentiate between the publication date of a page and other dates that may be embedded in its content.

Services: [html, url]

Fetch all result of Publication Date model

  @alchemy_service.publication_date.result

Relations

Relations - identifies subject, action, and object relations within sentences in the input content. After parsing sentences into subject, action, and object form, the Relation Extraction API functions can use this information for subsequent processing by other AlchemyLanguage functions such as entity extraction, keyword extraction, sentiment analysis, and location identification. Relation information can be used to automatically identify buying signals, key events, and other important actions.

Services: [html, url, text]

Fetch all result of Relations model

  @alchemy_service.relation(
    maxRetrieve: 1
  ).result

Typed Relations

Typed Relations - uses custom models to recognize entities in content, and identifies various types of relations between those entities. The types of entities and relations that can be identified are properties of the custom model that is specified. With the public custom model ie-en-news, Typed Relations can identify entities and relations related to the news domain. For example, given the input "Leonardo DiCaprio won an Oscar," a request using ie-en-news will recognize "Leonardo Dicaprio" as a "Person" entity and "Oscar" as an "EntertainmentAward" entity, and it will recognize an "awardedTo" relation that exists between those entities.

Services: [html, url, text]

Fetch all result of Typed Relations model

  @alchemy_service.typed_relation.result

Sentiment

Sentiment - identifies attitude, opinions, or feelings in the content that is being analyzed. The AlchemyLanguage service can calculate overall sentiment within a document, sentiment for user-specified targets, entity-level sentiment, quotation-level sentiment, directional-sentiment, and keyword-level sentiment. The combination of these capabilities supports a variety of use cases ranging from social media monitoring to trend analysis.

Services: [html, url, text]

Fetch all result of Sentiment model

  @alchemy_service.sentiment_analysis.result

Targeted Sentiment

Supported languages: Arabic, English, French, German, Italian, Portuguese, Russian, Spanish

Fetch all result of Targeted Sentiment model

  @alchemy_service.targeted_sentiment(
    targets: "Target 1|Taget 2"
  ).result

Taxonomy

Taxonomy - categorizes input text, HTML or web-based content into a hierarchical taxonomy up to five levels deep. Deeper levels allow you to classify content into more accurate and useful subsegments. The AlchemyLanguage taxonomy methods use an enhanced version of the standard Interactive Advertising Bureau Quality Assurance Guidelines Taxonomy, extending that taxonomy to over 1000 categories for superior audience targeting and information routing.

Services: [html, url, text]

Fetch all result of Taxonomy model

  @alchemy_service.taxonomy.result

Text Extraction

Text Extraction - AlchemyLanguage cleans source text from html in each API method by default. This includes normalizing HTML content, removing ads, navigation links, and other unimportant content so that only the important page text is returned.

Services: [html, url]

Fetch all result of Text Extraction model

Text (cleaned)

  @alchemy_service.text.result

Text (raw)

  @alchemy_service.raw_text.result

Title Extraction

Title Extraction - Extracts title information from web pages Services: [html, url]

Fetch all result of Title Extraction model

  @alchemy_service.title_extraction.result

Combined Call

Combined Call - AlchemyLanguage offers a wide variety of text analysis capabilities, but getting a wealth of semantic information for a single document is made simple with combined calls.

Services: [html, url, text]

Fetch all result of Combined Call model

  @alchemy_service.combined_call(
    extract: "entities,keywords",
    sentiment: 1,
    maxRetrieve: 1
  ).result

License

This project is licensed under the MIT License - see the LICENSE.md file for details