Using N-Gram Graphs for Sentiment Analysis: An Extended Study on Twitter

Abstract

Tackling the challenges posed by Social Networking content and addressing its casual nature, n-gram graphs technique provides a language-independent supervised approach for text mining. Adopting this data analysis model, this paper provides an extended study of sentiment analysis, using a multilingual and multi-topic environment, employing and combining different classification algorithms, and attempting various configuration approaches on classification parameters to increase the efficiency. Compared to results found on big corpora used in previous studies, the outcome of the current paper implies a high classification accuracy and an enhanced validity, since the current experiments use datasets processed by human annotators.