Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!

## Project description

Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!

## Sentiment Analysis

We provide three-class (positive, negative, objective-OR-neutral) sentiment analysis on tweets.

Experiments are ongoing, but currently the system uses a hierarchical classifier that first determines if a tweet is objective or subjective (subjectivity classifier), and then if subjective determine if the tweet is positive or negative (polarity classifier).

We use approximately 8,750 labeled training instances provided by the Sentiment Analysis in Twitter task for SemEval-2013. We then “freeze” the subjectivity classifier, as we currently haven’t been able to incorporate additional high quality labeled or unlabeled objective-OR-neutral tweets or text. However, we continue to train the polarity classifier through self-training on approximately 1 million unlabeled tweets that are likely to contain sentiment. The additional tweets were captured from Twitter if they had a matching emoticon present in the text of the tweet.

### SemEval-2013

An early version of our system was entered in the SemEval-2013 competition. Our simple system (Naive Bayes with unigrams + bigrams) scored 25th out of 48 submissions, which while not state-of-the-art is still not too bad.

The evaluation metric was the average F-measure of the positive and negative classes. Our system achieved an F-measure of 0.5437, while the top system achieved 0.6902.

#### Results of system for SemEval-2013

Confusion table:
gs \ pred| positive| negative|  neutral
---------------------------------------
positive|      841|      233|      498
negative|       74|      324|      203
neutral|      276|      196|     1168

Scores:
class                    prec                 recall     fscore
positive      (841/1191) 0.7061    (841/1572) 0.5350     0.6088
negative       (324/753) 0.4303     (324/601) 0.5391     0.4786
neutral      (1168/1869) 0.6249   (1168/1640) 0.7122     0.6657
--------------------------------------------------------------------
average(pos and neg)                                          0.5437

In the mean time, we have a lot more experimental ideas that may improve the performance of our classifier, so it’s time to get experimenting!

### RPC server

The sentiment analysis classifier can be loaded from file and served using a RPC server. This allows the classifier to potentially be used by many applications, as well as being able to stay loaded even if another application that depends on the classifier needs to restart or update.

### Web user interface

We have added a very simple web interface that allows users to query the system. Lots of upcoming features are planned for the web interface.

Known Bug: If installing the package through pip or setup.py then the web interface files under web/static and web/templates are not copied along with the installation. Therefore, either copy these files manually or run from the source directory.

### RESTful JSON API

GET sentiment/classify

#### Resource URL

http://…/api/sentiment/classify.json

#### Parameters

• text: String representing the document to be classified.

#### Response object fields

• text: String of the original input text.

• label: String of the sentiment classification label.

• confidence: Float of the confidence in the label.

#### Example request

GET http://.../api/sentiment/classify.json?text=Today+is+March+30%2C+2013.

{
"text": "Today is March 30, 2013.",
"confidence": 0.9876479882432573,
"label": "neutral"
}

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