Negation Handling in Sentiment Analysis at Sentence Level

Abstract

Sentiment analysis is an automatic way to determine that whether opinions of people about a subject are favorable or unfavorable. One of the most important sub tasks in sentiment analysis is to determine the sequence of words affected by negation. Most of the existing sentiment analysis systems used traditional methods based on static window and punctuation marks to determine the scope of negation. However, these methods do not offer satisfactory performance due to variability in the negation scope, inability to deal with linguistic features and improper word sense disambiguation. In this paper, we investigate the problem of identifying the scope of negation while determining the polarity of a sentence. We propose a negation handling method based on linguistic features which determine the effect of different types of negation. Experiment results show that the proposed method improves the accuracy of both negation scope identification and overall sentiment analysis.