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sentimentstwitter

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Sentiment Analysis on Twitter Datasets

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Sentiment Analysis on Twitter
=============================

### The Problem
Given a tweet (that contains some text), estimate the sentiment
(negative or positive) of the tweeter.
 
  
### Training, Development, and Test Datasets
Some folks at Stanford spent more than a year doing
research on sentiment analysis on twitter. 
They published a paper
 [here] (http://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)
and released both their training and test sets, which we used
throughout our project. 
  
The training set has 1,600,000 tweets marked positive/negative, while
the test set has 498 tweets. We extracted a development set of 
500 tweets from the original training set to use in adjusting 
various parameters for both types of classifiers.
  
### Methods Used
* **Naive Bayes Classifier:** 
  Using the "best"
  parameters on our naive bayes classifier, we achieved an accuracy
  of 83.01%. It turns out that our Naive Bayes classifier
  performs better than that of Alec Go's. The Naive Bayes
  classifier is implemented in 
  **naivebayesclassifier.py** and the Naive Bayes
  evaluator (which measures the effectiveness of the 
  classifier) is implemented in 
  **naivebayesevaluator.py**. 
  
  Run `python naivebayesevaluator.py -g 1 1,2` to see
  the accuracy on the test set. 
  
  Run `python naivebayesevaluator.py -h` to see all 
  options for the Naive Bayes Evaluator. For more details, see 
  documentation in the evaluator file.
  
* **Maximum Entropy Classifier:**  
  The input to an instance of the Maximum Entropy Evaluator
  is made up of four parameters:

* Number of tweets to train on (**filesubset**)
   * Minimum number of occurences a feature must have appeared
   to be included as a feature (**min_occurences**)
   * The number of iterations to run GIS (**max_iter**)
   * What **n**-grams to use (**grams**, a list)
  
  These parameters can be tweaked in **maxentevaluator**. Generally,
  we noted the intuitive trend that using more data gave better results.
  As a result, to get the best results, we would use a large subset
  of tweets and a small threshold  level of feature occurences 
  (i.e. including as many **n**-grams as possible). For practical purposes
  of demonstration, the parameters are set to lower values which only take
  a few minutes to run. 
  
  In order to use a better model (which achieves 76% on the test set),
  we've included a pickled model
  which was trained using 4000 tweets, unigrams and bigrams, and 
  a threshold of 3. When a Maximum Entropy Classifier is trained, the 
  resulting model is pickled to **maxentpickles**. While we
  have not included all the pickled models, the file 
  **maxent_4000_3_2.dat** is included and can be run via 
  **maxentevaluator**'s **runFromPickle** method. 
  
## Web Interface
  Our models were trained and evaluated
  on data accumulated in 2006. We thought it might be useful
  (and cool)
  to evaluate our models (Naive Bayes and Max Entropy)
  on more recent tweets using the 
  Twitter real-time API. So we setup a python Tornado
  server with a Graphical User Interface (built using
  HTML + CSS + Javascript) to grab tweets, perform
  sentiment analysis on these tweets, and display
  the results in an intuitive manner. 
  
  If you want to run the web server, you need
  tornado web (which can be easily intalled via 
  **pip** or **easy_install**. Use `python app.py` to startup the server. The file
  first loads both classifiers (takes about 2 minutes to load
  the models). Wait
  till you see the messages: `Model retrieved from 'model1-2.dat'` and `Max ent model built`. By 
  default, it runs on port 8888. To see the server in 
  action, using your favorite browser go to
  `http://localhost:8888/`. Pick what model you want
  to evaluate the tweet on, and **Search**.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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