Conference Paper

Opinion mining and summarization of reviews in web forums

DOI: 10.1145/1754288.1754312 Conference: Proceedings of the 3rd Bangalore Annual Compute Conference, Compute 2010, Bangalore, India, January 22-23, 2010
Source: DBLP


The Internet has made life of every individual (web users) very simple and sophisticated. In recent years people use the web for many reasons like personal communication, entertainment, online shopping, general search and so on. Internet forums also act as a medium of exchange for sharing resources and knowledge. Though commercial review websites allow users to express their opinions in whatever way they feel, number of reviews for specific product available is enormous. Hence it becomes difficult for the customers to read all the reviews to make a decision. In this paper we propose an extraction technique to score the reviews and summarize the opinions to end user. Based on opinions mined it is decided as whether to recommend the product to the user or not. This paper mainly focuses on providing a methodology for mining the opinions using generic user focused reviews. The experiments performed were quite promising for the data set used.

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    • "For example, a regular opinion is mostly used to find good or bad views about a particular product whereas a comparative opinion is significantly utilized for comparing two or more products (or simply to describe the competitive intelligence involved in these products). Existing work [4, 10–12] covers different aspects of regular opinions. "
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    ABSTRACT: Existing opinion mining studies have focused on and explored only two types of reviews, that is, regular and comparative. There is a visible gap in determining the useful review types from customers and designers perspective. Based on Technology Acceptance Model (TAM) and statistical measures we examine users' perception about different review types and its effects in terms of behavioral intention towards using online review system. By using sample of users (N = 400) and designers (N = 106), current research work studies three review types, A (regular), B (comparative), and C (suggestive), which are related to perceived usefulness, perceived ease of use, and behavioral intention. The study reveals that positive perception of the use of suggestive reviews improves users' decision making in business intelligence. The results also depict that type C (suggestive reviews) could be considered a new useful review type in addition to other types, A and B.
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    ABSTRACT: We investigate mechanisms that trigger collaborative work behavior in online peer communities. We regard the collaboration among Wikipedia editors as a social process influenced by specific communication practices. We analyze and quantify the way Wikipedia editors communicate their feedback and support towards each others' work in form of sentiments and opinions, and explore to what extent this influences online trust among them. We show that peer content production in Wikipedia is influenced by sharing sentiments during discussions among editors. At the global level, sharing sentiments positively influences the level of online trust. We also find a significant difference in the amount of online trust among editors who share mainly positive or mainly negative sentiments. We further suggest that providing and receiving especially supportive feedback expressed in form of positive sentiments and opinions may be beneficial in terms of virtual teamwork. © (2011) by the AIS/ICIS Administrative Office All rights reserved.
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    ABSTRACT: The explosion of the Web 2.0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.
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