Identifying the semantic orientation of terms using S-HAL for sentiment analysis

Abstract

Sentiment analysis continues to be a most important research problem due to its abundant applications. Identifying the semantic orientation of subjective terms (words or phrases) is a fundamental task for sentiment analysis. In this paper, we propose a new method for identifying the semantic orientation of subjective terms to perform sentiment analysis. The method takes a classification approach that is based on a novel semantic orientation representation model called S-HAL (Sentiment Hyperspace Analogue to Language). S-HAL basically produces a set of weighted features based on surrounding words, and characterizes the semantic orientation information of words via a specific feature space. Because the method incorporates the idea underlying HAL and the hypothesis verified by the method of semantic orientation inference from pointwise mutual information (SO-PMI), it can quickly and accurately identify the semantic orientation of terms without the use of an Internet search engine. The results of an empirical evaluation show that our method outperforms other known methods.