Sentiment Analysis on tweets using n-grams and lexicon

Abstract

Twitter is serving as micro-blogging platform where freedom is given to user to express their opinion and share information about any subject through short messages known as tweets. Tweet composed of textual data that can be classified into positive, negative or neutral sentiment. This classification is often based on the analysis of n-grams, which involves examining the frequency and combination of words used. This research article presents a technique which filters out the noise from tweets and apply the scoring mechanism to sentiments that assigns the score between -1 and +1. The proposed techniques results are validated by manually scored evaluations for same tweets. Additionally, the study compares the effectiveness of different n-gram techniques for sentiment analysis.