Application of Opinion Mining in E-Commerce

Application of Opinion Mining in E-Commerce

Brojo Kishore Mishra (C. V. Raman College of Engineering, India) and Rekhanjali Sahoo (BPUT Rourkela, India)
DOI: 10.4018/978-1-5225-3646-8.ch012
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Abstract

Social media has become an increasingly important part of our daily lives in the last few years. An area of substantial research is that of predicting product sales, such as books, video games, and movie tickets. Customer opinions play a very crucial role in daily life. When we have to make decisions, opinions of other individuals are also considered. Business organizations and corporate organizations are always eager to find consumer or individual views regarding their products, support, and service. In e-commerce, online shopping, and online tourism, it is very crucial to analyze the social data present on the web automatically; therefore, it is very important to create methods that automatically classify them. In this chapter, the authors do future prediction of financial market by taking past observations so that they can maximize their profit and orders can be placed on time.
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Introduction

Opinion Mining or sentiment classification involves building a system to make use of reviews posted by the users and opinions that are expressed in blogs and forums as comments and reviews and sometimes may be as tweets about the product (Mining Hu & Bing Liu, 2004). Opinions are written on many things example a product, a topic, an individual, etc. In opinion mining task we identify the orientation of opinion by the holder towards any object which may be a collection of features or components or attributes. Opinion Mining sometimes called as Sentiment Classification is defined as mining and analyzing of reviews, views, emotions and opinions automatically from text, big data and speech by means of various methods (Andrea Esuli, 2008). Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information-information that can be used to increase revenue, cuts costs, or both. (Mining Hu & Bing Liu, 2004). In our proposed object Opinion mining helps in e-commerce for taking decision to buy any product. In e-commerce, online shopping and online tourism, it’s very crucial to analyze the good amount of social data present on the Web automatically therefore, it’s very important to create methods that automatically classify them. Opinion Mining sometimes called as Sentiment Classification is defined as mining and analyzing of reviews, views, emotions and opinions automatically from text, big data and speech by means of various methods (Mining Hu & Bing Liu, 2004). Our main theme is to create a system for analyzing opinions which implies judgment of different consumer products. In our proposed project for sales prediction we will take into consideration the present date for future prediction.

To increase the buyers know more about the credit of E-commerce product sellers and the purchase rate of the E-commerce users, E-commerce credit evaluation model based on the opinion mining algorithm was put forward (Alexander Pak and Patrick Paroubek, 2010). Extract the feature words and views from the products and user reviews, and then make use of statistical and quantitative way to analyze them. In the mean time, a credit evaluation model with transaction time-frequency can be set up, which can be used to analyze the seller’s credit of E-commerce users.

Opinion Mining

Given a set of evaluative text documents D that contain opinions (or sentiments) about an object, opinion mining aims to extract attributes and components of the object that have been commented on in each document d ∈D and to determine whether the comments are positive, negative or neutral (Antweiler & Frank, 2004).

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