Comparative Analysis of Lexicon-Based Sentiment Analysis Methods

Abstract

Sentiment Analysis studies the opinions, sentiments and emotions expressed at sentence or document level.  Machine learning and lexicon-based approaches have been successfully used to achieve this.  This paper will focus on the lexicon-based approach. In contrast to most existing research, we compare the effectiveness of multiple dictionaries across a series of datasets related to public order events. The comparison will look to understand the possible benefits and limitations of sentiment analysis methods, which will bench marked against each other in their evaluation of results. The evaluation will be based four labelled datasets, covering messages on posts related to public order events. The results will highlight the extent of how well each of these methods perform across the datasets comparing sentence level analysis with range of sentiment analysis techniques.