Verb Oriented Sentiment Classification

Abstract

Sentiment analysis refers to a broad range of fields of natural language processing, computational linguistics and text mining. Sentiment classification of reviews and comments has emerged as the most useful application in the area of sentiment analysis. Although sentiment classification generally is carried out at the document level, accurate results require analysis at the sentence level. Bag of words and feature based sentiment are the most popular approaches used by researchers to deal with sentiment classification of opinions about products such as movies, electronics, cars etc. Until recently most classification techniques have considered adjectives, adverbs and nouns as features. This paper proposes a new approach based on verb as an important opinion term particularly in social domains. We extract opinion structures which consider verb as the core element. Sentiment orientation is recognized from sentiments inside of opinion structures and their association with the social issue. Experimental results show that considering verbs improves the performance of sentiment classification.