Abstract
Sentiment analysis is widely studied to extract opinions from user generated content (UGC), and various methods have been proposed in recent literature. However, these methods are likely to introduce sentiment bias, and the classification results tend to be positive or negative, especially for the lexicon-based sentiment classification methods. The existence of sentiment bias leads to poor performance of sentiment analysis. To deal with this problem, we propose a novel sentiment bias processing strategy which can be applied to the lexicon-based sentiment analysis method. Weight and threshold parameters learned from a small training set are introduced into the lexicon-based sentiment scoring formula, and then the formula is used to classify the reviews. In this paper, a completed sentiment classification framework is proposed. SentiWordNet (SWN) is used as the experimental sentiment lexicon, and review data of four products collected from Amazon are used as the experimental datasets. Experimental results show that the bias processing strategy reduces polarity bias rate (PBR) and improves performance of the lexicon-based sentiment analysis method.
Highlights
Social media sites have been growing exponentially in recent years
We propose a novel method to process sentiment bias, and a lexicon-based sentiment analysis framework is designed with the sentiment bias processing strategy
The proposed method can significantly improve the performance of lexicon-based sentiment analysis method by reducing sentiment bias
Summary
Social media sites have been growing exponentially in recent years They have already become important and popular platforms for people to express their emotions, opinions, experiences etc. Sentiment bias widely exists in lexicon-based methods especially when general purpose sentiment lexicons are used, which leads to poor and imbalanced polarity classification results. We propose a novel method to process sentiment bias, and a lexicon-based sentiment analysis framework is designed with the sentiment bias processing strategy. The proposed method can significantly improve the performance of lexicon-based sentiment analysis method by reducing sentiment bias. We present a sentiment bias processing strategy for the lexicon-based sentiment analysis method. 2. We design a SWN-based sentiment analysis framework which uses the proposed sentiment bias processing strategy.
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