Sunday, March 31, 2013

Online Tools for Sentiment Classification - Part I

Seeing Sentiment Analysis in action is a good way of getting a feel for what the tasks are about, and what techniques are in use today. In this post I survey some of the research interfaces available online which you can use on your favorite piece of text. 

Christopher Potts Tutorial - Text Scoring Demo
In this tutorial demo by Stanford Professor Christopher Potts, an engine that analyses text and classifies sentiment according to hits from different sentiment lexicons.

His tutorial page also links to code demos in Python showcasing a number of techniques for Sentiment Analysis. 


TweetFeel is a polished UI for quickly inspecting sentiment on twitter posts, but is not so much a research project and more a demo of a commercial product (there is not a lot of details of underlying techniques here). My tests revealed much dismay on the twittersphere at Ireland's squad recent results at the WC qualifiers :(

Sentimentor is a project from Univ. of Brighton's James Spencer. The output of this tool is a visual display of how each term was scored with respect to sentiment orientation. 

The method and code behind this interface is available upon request from the author.


Sentiment140 is a project from Stanford University students Alec GoRicha Bhayani, and Lei Huang, based on their work on using machine learning algorithms to classify twitter sentiment. (requires a twitter account)


SentiwordNet is a sentiment lexicon built from WordNet. It assigns sentiment orientation to words by expanding an initial seed set by inspecting term relationships and sentiment glosses. The UI allows querying for a term, giving back numeric scores for a tuple (positive, negative, objective)

The lexicon is described on this paper (see here for v1.0), and is also available for download.

Other Resources

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