Twitter Based Sentiment Analysis

Abstract

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings
towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers
for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel
approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted
entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and
measure the correlation of the representative concept with negative/positive sentiment. The social web has made
enormous amounts of information available to users globally at just the click of a button. Consumers often tend to rely
on such text, especially those in the form of opinions or experiences regarding a particular product which makes it
essential that this information should be available in a systematic manner. Sentiment analysis studies these opinions.
This paper explains different methods for sentiment analysis and showcases an efficient methodology. It also
highlights the importance of Naïve Bayes classifier over other classification algorithms.