Temporal sentiment analysis and time tags for opinions

Abstract

Nowadays, opinion mining becomes one of the most important fields and it attracts the interest of many researchers. The ‘electronic Word of Mouth’ (eWOM) statements that are expressed on the web, are important for business and service industry to enable customers share their point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on opinion mining — which is also called, sentiment analysis to identify and categorize opinions from a piece of text. One key use of sentiment analysis is to extract and analyze public moods and views. Researches used sentiment analysis in different ways. For example, to determine market strategy, to improve customer service. One of the key challenges of sentiment analysis is how to extract temporal synsets from text. Temporal synsets may be events, dates, times, or even Explicit lyrics. Tempowordnet is one of the attempts to building a lexicon that may help in finding temporal synsets. In this paper, we propose a framework for discovering temporal verb references (future, past and present) from opinions and using them to build accurate prediction models. The proposed method improved the percentage of discovering the(past, present and future verbs)over the tempowordnet.