Ontology-Based Sentiment Analysis Model of Customer Reviews for Electronic Products

Ontology-Based Sentiment Analysis Model of Customer Reviews for Electronic Products

Kin Meng Sam, Chris Chatwin
Copyright: © 2015 |Pages: 13
DOI: 10.4018/978-1-4666-5888-2.ch085
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Background

Popularity of Social Networking Websites

A social networking site is a website in which Internet users can gather and share information, develop friendships, and build professional relationships. Nielsen (2010a) stated that the average social networking visitor is now spending almost 6 hours per month in April 2010, versus 3 hours, 31 minutes in April 2009, an increase of almost 60% in one year. According to a recent survey more than half of all marketers are currently engaging in some form of social media activity with about 60% of them planning on increasing their spending in the near future (Ramsey, 2010). After friends and family, the number one driver for brand trust is online reviews and feedback from social networking (Nielsen, 2010b). As a direct result, advertisers are moving from a more traditional broadcasting based marketing relationship with online consumers to a more interactive based marketing relationship, where consumers directly engage with marketing messages and pass them along to their friends via social networking sites (Gibs & Bruich, 2010). As a result, the social networking sites can greatly affect online consumers’ buying decisions. Through social networking sites, Internet users can exchange information, thoughts and ideas about certain products or services. Through these sites, online companies can search informative material to help them analyze product trends in the market.

Text-Mining in Social Networking Sites

Text mining emerged as a means to derive knowledge from unstructured data, especially data available on the World Wide Web. Mine, Lu and Amamiya (Mine, Lu & Amamiya, 2002) suggest a text mining system that obtains the relationship between the topics at international conferences. This experiment promises that the method works not only for obtaining the relationship between topics of conferences, but also for discovering the relationship between information entities that users are interested in. The issues regarding text mining have been discussed by Castellano and Mastronardi (Castellano, Mastronardi, Aprile & Tarricone, 2007) who have developed a web text mining flexible architecture, which can discover knowledge in a distributed and heterogeneous multi-organization environment. Pierre and Blondel (2008) offer ways for discovery of similar words from the WWW corpus. However both these works were based on text that follows a set of English grammar standard syntax. The mined information is therefore based on predetermined relationships using specific rules and ontologies. However, with the advent of social networking websites, a lot of information is in a non-rule based textual format. The usefulness of this information to determine social behavior is demonstrated by Java, Song, Finin and Tseng (2007). In view of this development, it becomes essential to extract behavioral patterns from relationships established using these data sources rather than predefined associations in ontologies.

Key Terms in this Chapter

Semantic Content Retrieval: The process of getting the meaning from the content.

Emotional Tolerance Index: The tolerance level specified by users to accept emotional difference between query and user posts.

Average Emotional Level Difference (ALD): The average emotional difference between all emotional keywords of a user post and the emotional keyword of query.

Ontology: A set of representational primitives used to model a domain of knowledge.

Sentiment Analysis: The extraction of positive or negative opinions from unstructured text.

Soft Parsing: An analysis of a sentence, which identifies the constituents (noun groups, verbs, verb groups, etc.)

Query Analysis: The analysis of the keywords in a query in order to understand the meaning of the query.

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