TwiSent: A Multistage System for Analyzing Sentiment in Twitter
This is an incremental improvement for researchers and practitioners needing more accurate sentiment analysis on noisy Twitter data.
The authors tackled sentiment analysis on Twitter by developing TwiSent, a system that addresses challenges like spam, text anomalies, entity specificity, and pragmatics, and showed it performs better than an existing system on manually annotated data.
In this paper, we present TwiSent, a sentiment analysis system for Twitter. Based on the topic searched, TwiSent collects tweets pertaining to it and categorizes them into the different polarity classes positive, negative and objective. However, analyzing micro-blog posts have many inherent challenges compared to the other text genres. Through TwiSent, we address the problems of 1) Spams pertaining to sentiment analysis in Twitter, 2) Structural anomalies in the text in the form of incorrect spellings, nonstandard abbreviations, slangs etc., 3) Entity specificity in the context of the topic searched and 4) Pragmatics embedded in text. The system performance is evaluated on manually annotated gold standard data and on an automatically annotated tweet set based on hashtags. It is a common practise to show the efficacy of a supervised system on an automatically annotated dataset. However, we show that such a system achieves lesser classification accurcy when tested on generic twitter dataset. We also show that our system performs much better than an existing system.