Detecting Sentiments from Movie Reviews by Integrating Reviewers Own Prejudice

Presently, sentiment analysis algorithms are widely used to extract positive or negative feedback scores of various objects on the basis of the text/reviews. But, an individual may have a certain degree of biasness towards a certain product/company and hence may not objectively review the object. We try to combat this biasness problem by incorporating the positive and negative bias component in the existing sentiment score of the object. This paper proposes several algorithms for a new system of implementing individual bias in the corpus of data i.e. movie reviews in this case. Each review comment has an unadjusted sentiment score associated with it. This unadjusted score is refined to give an adjusted score using the positive and negative bias score. The bias score is calculated using certain parameters, the weightage of which has been determined by conducting a survey. We lay emphasis on the degree of biasness an individual has towards or against the review parameters for the movie reviews corpus namely actor, director and genre. We equip the system with the capability to handle various scenarios like positive inclination of the user, negative inclination of the user, presence of both positive and negative inclination of the user and neutral attitude of the user by implementing the formulae we developed

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

An Empirical Study On Sentiment Polarity Classification Of Book Reviews

Sentiment polarity classification deals with automatic classification of text in sentiment polarity categories. While in most of proposed approaches for polarity classification, a dictionary containing polarity-based terms is considered. Such dictionaries are not readily available. We have adopted a machine learning based approach where classifiers are trained over a self-collected corpus of book reviews, annotated with sentimental categories. In this paper, we have presented our investigation of performance evaluation of machine learning classifiers. Five classifiers are evaluated including naïve Bayes, k-nearest neighborer, decision tree and support vector machine. Naïve Bayes has shown us best results

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

A comparative study of Bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection

Exploring Cross-National Differences in Online Review Topics between China and the United States

The fast growing cross-border e-commerce makes it imperative for online merchants to deeply understand the cross-national differences in consumers’ preferences and online shopping behaviors. Using a data-driven topic model, this study plans to investigate the semantic differences in online product reviews posted by consumers from China and the United Sates. The preliminary results from a pilot study of online reviews of books show that Chinese reviewers focus more on a product’s concrete attributes while American reviewers prefer to express their general evaluations of the product

SENTIMENT STRENGTH AND TOPIC RECOGNITION IN SENTIMENT ANALYSIS

Current sentiment analysis methods focus on determining the sentiment polarities (negative, neutral or positive) in users’ sentiments. However, in order to correctly classify users’ sentiments into their right polarities, the strengths of these sentiments must be considered. In addition to classifying users’ sentiments into their correct polarities, it is important to determine the sources and topics under which users’ sentiments fall. Sentiment strength helps as to understand the levels of customer satisfaction toward products and services. Sentiment topics on the other hand, helps to determine the specific product/service areas associated with user sentiments. This paper proposes two sentiment analysis approaches. First an approach which determines the sentiment strength expressed by consumers in terms of a scale (highly positive, +5 to highly negative, -5) is proposed. The approach includes a novel algorithm to compute the strength of sentiment polarity for each text by including the weights of the words used in the texts. Second, a sentiment mining approach which detects sentiment topic from text is proposed. The approach includes a sentiment topic recognition model that is based on Correlated Topics Models (CTM) with Variational Expectation-Maximization (VEM) algorithm. Finally, the effectiveness and efficiency of these models is validated using airline data from Twitter and customer review dataset from amazon.com --Abstract, p. ii