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Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!
Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!
|Build Status|_
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 <http://www.cs.york.ac.uk/semeval-2013/task2/>
_ 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"
}
.. |Build Status| image:: https://travis-ci.org/bwbaugh/infertweet.png?branch=master
.. _Build Status: https://travis-ci.org/bwbaugh/infertweet
FAQs
Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!
We found that infertweet demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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