A new ANEW: Evaluation of a word list for sentiment analysis in microblogs

TL;DR

This paper evaluates the effectiveness of existing and new sentiment lexicons for analyzing sentiment in microblogs like Twitter, finding that a newly constructed list may outperform older lexicons like ANEW.

Contribution

It introduces a new sentiment word list tailored for microblogs and compares its performance against ANEW and other methods.

Findings

The new word list performs better than ANEW in microblog sentiment detection.

Simple word matching with the new list approaches the performance of more complex methods.

ANEW is less effective for microblog sentiment analysis compared to specialized lists.

Abstract

Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, -- a 'sentiment lexicon' or 'affective word lists'. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) developed before the advent of microblogging and sentiment analysis. I wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs. I used manually labeled postings from Twitter scored for sentiment. Using a simple word matching I show that the new word list may perform better than ANEW, though not as good as the more elaborate approach found in SentiStrength.