Sentiment Analysis of Product Reviews

Sentiment Analysis of Product Reviews

Cane W.K. Leung (The Hong Kong Polytechnic University, Hong Kong SAR, China)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch273
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Abstract

Sentiment analysis is a kind of text classification that classifies texts based on the sentimental orientation (SO) of opinions they contain. Sentiment analysis of product reviews has recently become very popular in text mining and computational linguistics research. The following example provides an overall idea of the challenge. The sentences below are extracted from a movie review on the Internet Movie Database: “It is quite boring...... the acting is brilliant, especially Massimo Troisi.” In the example, the author stated that “it” (the movie) is quite boring but the acting is brilliant. Understanding such sentiments involves several tasks. Firstly, evaluative terms expressing opinions must be extracted from the review. Secondly, the SO, or the polarity, of the opinions must be determined. For instance, “boring” and “brilliant” respectively carry a negative and a positive opinion. Thirdly, the opinion strength, or the intensity, of an opinion should also be determined. For instance, both “brilliant” and “good” indicate positive opinions, but “brilliant” obviously implies a stronger preference. Finally, the review is classified with respect to sentiment classes, such as Positive and Negative, based on the SO of the opinions it contains.
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Introduction

Sentiment analysis is a kind of text classification that classifies texts based on the sentimental orientation (SO) of opinions they contain. Sentiment analysis of product reviews has recently become very popular in text mining and computational linguistics research. The following example provides an overall idea of the challenge. The sentences below are extracted from a movie review on the Internet Movie Database:

“It is quite boring...... the acting is brilliant, especially Massimo Troisi.”

In the example, the author stated that “it” (the movie) is quite boring but the acting is brilliant. Understanding such sentiments involves several tasks. Firstly, evaluative terms expressing opinions must be extracted from the review. Secondly, the SO, or the polarity, of the opinions must be determined. For instance, “boring” and “brilliant” respectively carry a negative and a positive opinion. Thirdly, the opinion strength, or the intensity, of an opinion should also be determined. For instance, both “brilliant” and “good” indicate positive opinions, but “brilliant” obviously implies a stronger preference. Finally, the review is classified with respect to sentiment classes, such as Positive and Negative, based on the SO of the opinions it contains.

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Background

Sentiment analysis is also known as opinion mining, opinion extraction and affects analysis in the literature. Further, the terms sentiment analysis and sentiment classification have sometimes been used interchangeably. It is useful, however, to distinguish between two subtly different concepts. In this article, hence, sentiment analysis is defined as a complete process of extracting and understanding the sentiments being expressed in text documents, whereas sentiment classification is the task of assigning class labels to the documents, or segments of the documents, to indicate their SO.

Sentiment analysis can be conducted at various levels. Word level analysis determines the SO of an opinion word or a phrase (Kamps et al., 2004; Kim and Hovy, 2004; Takamura and Inui, 2007). Sentence level and document level analyses determine the dominant or overall SO of a sentence and a document respectively (Hu and Liu, 2004a; Leung et al., 2008). The main essence of such analyses is that a sentence or a document may contain a mixture of positive and negative opinions. Some existing work involves analysis at different levels. Specifically, the SO of opinion words or phrases can be aggregated to determine the overall SO of a sentence (Hu and Liu, 2004a) or that of a review (Turney, 2002; Dave et al., 2003; Leung et al., 2008).

Most existing sentiment analysis algorithms were designed for binary classification, meaning that they assign opinions or reviews to bipolar classes such as Positive or Negative (Turney, 2002; Pang et al., 2002; Dave et al., 2003). Some recently proposed algorithms extend binary sentiment classification to classify reviews with respect to multi-point rating scales, a problem known as rating inference (Pang and Lee, 2005; Goldberg and Zhu, 2006; Leung et al., 2008). Rating inference can be viewed as a multi-category classification problem, in which the class labels are scalar ratings such as 1 to 5 “stars”.

Some sentiment analysis algorithms aim at summarizing the opinions expressed in reviews towards a given product or its features (Hu and Liu, 2004a; Gamon et al., 2005). Note that such sentiment summarization also involves the classification of opinions according to their SO as a subtask, and that it is different from classical document summarization, which is about identifying the key sentences in a document to summarize its major ideas.

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