> A sentiment analysis library for tweet objects and strings. 

> A sentiment analysis library for tweet objects and strings

michaelcheng

sentimentjs

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    "test": "echo \"Error: no test specified\" && exit 1"

    "url": "https://github.com/crowd-parser/sentimentjs.git"

  "author": "Michael Cheng <cheng.c.mike@gmail.com> (http://michaelcheng.io)",

    "url": "https://github.com/crowd-parser/sentimentjs/issues"

  "homepage": "https://github.com/crowd-parser/sentimentjs",

This sentiment library is used in our Crowd Parser app, which analyzes Twitter sentiment. Check it out here: 

[Crowd Parser](https://github.com/crowd-parser/crowd-parser)

Our goal with `sentimentjs` is to improve upon other sentiment libraries by including "layers" that check for factors that other sentiment libraries might ignore or miss.

For example, our "emoticons layer" specifically looks for emoticons in a string or tweet and gauges sentiment based upon its findings.

In the future, we hope to add more "layers," such as a "negation layer" and perhaps a "movie layer" that catches normal words that are actually movies.

Our base word lists come from renowned researchers Minqing Hu and Bing Liu, who authored this paper

_Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews."_

_Proceedings of the ACM SIGKDD International Conference on Knowledge_ 

_Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, Washington, USA_

var arrayOfStrings = ['I love deep dish pizza :)', 'I hate brussel sprouts >:('];

var sentimentStringsAnalysis = sentiment.stringsArray(arrayOfStrings);

Running the above will return an object that has this format:

To analyze tweet objects, you will need to have Twitter API credentials (keys, token, and secret). The README for our Crowd Parser app contains instructions for setting this up.

To set up the Twitter API with a Node server, this is how we do it:

  access_token_secret: 'ENTER YOURS HERE'

First, navigate to the `example` directory.

Finally, open `http://localhost:3000` in your browser.

To test, edit the array of strings in `server.js`. You can also try to use the `tweetsArray` method and enter an array of tweets.

var results = allLayersAnalysis.stringsArray(['I love dogs. They are wonderful! 😍 😍', 'I hate brussel sprouts. They are terrible. 😾', 'This is great! But also bad.']);

Change the above array for testing. Again, the sample output can be displayed like this:

There is a function in `index.html` to highlight the positive and negative words in the text.

We welcome you to join us in creating a better sentiment library! Our library is still in an infant stage, so contributions would be greatly appreciated!

		{
		"name": "sentimentjs",
		"version": "1.0.2",
		"version": "1.0.3",
		"description": "",
		@@ -5,0 +5,0 @@ "main": "index.js",

		@@ -5,2 +5,4 @@ # sentimentjs

		<img src="example-screenshot.png">

		This sentiment library is used in our Crowd Parser app, which analyzes Twitter sentiment. Check it out here:
		@@ -156,4 +158,40 @@

		## Using the Example

		First, navigate to the `example` directory.

		```
		cd example
		```

		Next, install dependencies:

		```
		npm install -g bower
		npm install
		bower install
		```

		Next, start the server:

		```
		node server
		```

		Finally, open `http://localhost:3000` in your browser.

		To test, edit the array of strings in `server.js`. You can also try to use the `tweetsArray` method and enter an array of tweets.

		```
		var results = allLayersAnalysis.stringsArray(['I love dogs. They are wonderful! 😍 😍', 'I hate brussel sprouts. They are terrible. 😾', 'This is great! But also bad.']);
		```

		Change the above array for testing. Again, the sample output can be displayed like this:

		<img src="example-screenshot.png">

		There is a function in `index.html` to highlight the positive and negative words in the text.

		## Contributing

		We welcome you to join us in creating a better sentiment library! Our library is still in an infant stage, so contributions would be greatly appreciated!

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