An Opinion Mining Model for Generic Domains
Online users are talking across social media sites, on public forums and within customer feedback channels about products, services and their experiences, as well as their likes and dislikes. The continuous monitoring of reviews is ever more important in
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An Opinion Mining Model for Generic Domains Franco Tuveri and Manuela Angioni
Abstract Online users are talking across social media sites, on public forums and within customer feedback channels about products, services and their experiences, as well as their likes and dislikes. The continuous monitoring of reviews is ever more important in order to identify leading topics and content categories and to understand how those topics and categories are relevant to customers according to their habits. In this context, the chapter proposes an Opinion Mining model to analyze and summarize reviews related to generic categories of products and services. The process, based on a linguistic approach to the analysis of the opinions expressed, includes the extraction of features terms from the reviews in generic domains. It is also capable to determine the positive or negative valence of the identified features exploiting FreeWordNet, a WordNet-based linguistic resource of adjectives and adverbs involved in the whole process. Keywords Opinion mining · Sentiment analysis extraction · Opinion summarization
· Text categorization · Feature
1 Introduction Reviews are used every day by common people or by companies who need to make decisions. They facilitate to book a hotel or a restaurant, to buy a book, or to taste the market tracing the customer satisfaction about a product. It is evident that the opinion monitoring is essential for listening to and taking advantage of the conversations of F. Tuveri (B) · M. Angioni CRS4, Center of Advanced Studies, Research and Development in Sardinia, Sardinia, Italy e-mail: [email protected] M. Angioni e-mail: [email protected]
C. Lai et al. (eds.), Distributed Systems and Applications of Information Filtering and Retrieval, Studies in Computational Intelligence 515, DOI: 10.1007/978-3-642-40621-8_3, © Springer-Verlag Berlin Heidelberg 2014
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possible customers in a data-driven decision making process or in order to elaborate strategies of marketing research. Researches about Opinion Mining, also called Sentiment Analysis, are passing through the simple evaluation of the polarity of the expressed feeling, to a deeper analysis of contents where opinions extracted are context related and the information about products and services are more detailed. Because of the overwhelming amount of information available new automatic tools are even more requested and appreciated especially by large organizations that track not only brands but even consumer preferences and opinions. A Gartner analysis for the 2012-year [1] illustrates the expectations about emerging technologies and how the need for automated methods is growing and social media analytics offers an answer [2], as one of the key themes emerging in the near future. The last “Sentiment Analysis Symposium”, hosted by Seth Grimes in New York City, evidenced the state of research about sentiment analysis, bridging technology and business in discovering business values in opinions and attitudes in social media, news, an
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