Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias

PloS One
Hongyu HanXiaomei Zou

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.

References

Aug 20, 2002·Annual Review of Psychology·James W PennebakerKate G Niederhoffer

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Citations

Aug 20, 2020·The Cleft Palate-craniofacial Journal : Official Publication of the American Cleft Palate-Craniofacial Association·Aylin Pasaoglu Bozkurt, Isıl Aras

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Software Mentioned

SO
BAT
CAL
Semantic Orientation CALculator ( SO - CAL )
POS Tagger
NLTK ( Natural Language Toolkit )

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