Abstract
Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.
Highlights
In the present age, it has become common practice for people to communicate or express their opinions and feedbacks on various aspects affecting their daily life through some form of social media
Recent papers in this field have pointed out that the task of opinion mining is sensitive to such hedges and taking the effect of linguistic hedges into consideration can improve the efficiency of the sentiment classification task [8, 12,13,14,15,16,17]
We classify a new user review based on its fuzzy sentiment score whose computation requires three steps: (1) extract features, associated descriptors, and hedges from the review based on FOLH table lookup, (2) identify the polarity and initial value of the feature descriptors based on SentiWordNet score, and (3) calculate overall sentiment score using fuzzy functions to incorporate the effect of linguistic hedges
Summary
It has become common practice for people to communicate or express their opinions and feedbacks on various aspects affecting their daily life through some form of social media. An upsurge in online activities like blogging, social networking, emailing, review posting, and so forth has resulted in incremental accumulation of a lot of user-generated content Most of these online interactions are in the form of natural language text. Zadeh developed the concept of fuzzy linguistic variables and linguistic hedges that modify the meaning and intensity of their operands [10, 11] Recent papers in this field have pointed out that the task of opinion mining is sensitive to such hedges and taking the effect of linguistic hedges into consideration can improve the efficiency of the sentiment classification task [8, 12,13,14,15,16,17]. We have proposed an approach to perform fine-grained sentiment classification of online product reviews by incorporating the effect of fuzzy linguistic hedges on opinion descriptors. We conclude and give directions for future work in this field
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