International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 03 | Mar -2017
e-ISSN: 2395 -0056
www.irjet.net
p-ISSN: 2395-0072
Sentiment Analysis of Product Reviews and Trustworthiness Evaluation using TRS Vivek Panchal1, Zaineb Penwala2, Sneha Prabhu3, Rhea Shetty4, Prof. Reena Mahe5 1,2,3,4
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Student, Dept. of Information Technology, Atharva College of Engineering, Maharashtra, India
Asst. Professor, Dept. of Information Technology, Atharva College of Engineering, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------Presently, most of the leading e-commerce websites, Abstract— There has been a rapid growth in the E-commerce industry which market and sell products as well as allow users to express their opinions about products. Buyers generally refer to these reviews and opinions before making a buying decision and to obtain first-hand experience of the product from certified buyers. However, users tend to adapt modern writing styles such as abbreviations, misspelled words, phrases instead of sentences and emoticons. Hence, automatic summarization of product reviews using Sentiment Analysis has great significance as it helps the company know what the user liked and disliked about the product as well as helps buyers make an online purchase decision. The basic task of sentiment analysis is sentiment classification which classifies a user review as positive, negative, neutral. Also, it is important to calculate the degree of trust of the user who posts a review, the review’s trustworthiness and generation of a global reputation score of the product. Keywords— Abbreviations, Opinions, Review, Sentiment Analysis, Summarization, Trustworthiness
1. INTRODUCTION Sentiment Analysis or Opinion Mining is the study of people’s opinions, attitudes and emotions toward an entity. An entity represents events or topics. These topics are most likely to be covered by reviews. Sentiment Analysis identifies the sentiment expressed in a text and then analyzes it. Therefore, the target of Sentiment Analysis is to find opinions, identify the sentiments expressed, and then classify their polarity. The data sets used in SA are important. The main sources of data are from the product reviews. These reviews are important to the business holders as they can take business decisions according to the analysis results of users’ opinions about their products. The review sources are mainly review sites [1]. Consumers make a better choice, if they have access to reviews and experiences from other consumers who have made similar choices. Mistakes that other consumers made can be avoided and confusion about products or services can be cleared.
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tend to focus on the product or its features and give very little emphasis to what is being said about the product. This paper focuses on semantically analyzing and evaluating product reviews. It does not compare products feature-wise but detects sentiments in a review and gives an overall rating. It also determines the degree of trust of the user providing the review by assigning it a global reputation score [3]. This is done by providing the user with critic reviews and asking user to like or dislike a particular critic review. Thus, sentiment of review provided can be evaluated and degree of trust of review determined through feedback system. However, few of them such as [6, 7, and 8] have been devoted to the semantic analysis of textual feedbacks in order to generate a most trustful trust degree of the user.
2. HISTORY The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by precision and recall. However, according to research human raters typically agree 79% of the time [4]. Thus, a 70% accurate program is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about any answer. For sentiment analysis tasks returning a scale rather than a binary judgment, correlation is a better measure than precision because it considers how close the predicted value is to the target value [4]. The system design of the opinion summarizer cum classifier has been implemented by L. Zhao and C. Li in this paper [5]. An opinion review database was generated by crawling some popular websites that categorically post product reviews by actual users. The product opinion summarizer consisted of three main phases that are: (1) Preprocessing phase (2) Feature extraction phase, and (3) Opinion summarization and classification phase [6]. Trust is an important factor in any social relationship, especially in commerce transactions. In the e-commerce
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