Capturing Contextual Factors in Sentiment Classification: An Ensemble Approach

Abstract

Sentiment classification is a crucial task in sentiment analysis, and has received significant attention from researchers. Previous studies have focused on using several techniques to solve this problem. However, to the best of our knowledge, none of these works has fully investigated the exploitation and the manipulation of contextual information in the text, or taken advantage of the combined power of state-of-the-art models. In this paper, we propose an effective ensemble learning model for the sentiment classification problem. In our system, the contextual information in the text is fully captured by integrating rule-based methods and other state-of-the-art deep learning models. We found that the combination of word embedding representation and the attention mechanism, along with pre-defined rules and specific-domain sentiment dictionaries are helpful in dealing with numerous valence-shifting cases. Although the computational cost of the proposed system is higher than those of certain other algorithms, this system obtains better results than other approaches when tested on three different datasets.

Keywords

• Sentiment classification
• contextual valence shifters
• ensemble learning
• deep learning
• attention mechanism