Data mining and knowledge discovery in databases
Communications of the ACM
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry
Journal of Management Information Systems
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects
Information Technology and Management
Blog Mining for the Fortune 500
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
An integrative approach to assess qualitative and quantitative consumer feedback
Electronic Commerce Research
Competing on Analytics: The New Science of Winning
Competing on Analytics: The New Science of Winning
Virtual communities: A marketing perspective
Decision Support Systems
Voice of the customers: mining online customer reviews for product feature-based ranking
WOSN'10 Proceedings of the 3rd conference on Online social networks
Aspect ranking: identifying important product aspects from online consumer reviews
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Electronic word-of-mouth and online reviews in tourism services: the use of twitter by tourists
Electronic Commerce Research
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While data mining is well established in practice, opinion mining is still in its infancy, with issues in particular around the development of methodologies which effectively extract accurate, reliable, influential and useful information from the raw opinion data collected from informal product reviews. Current approaches adopt a single-variable approach, focusing on individual metrics--word length, the presence of keywords, or the overall semantic orientation of terms within the data--while neglecting to evaluate whether these individual artifacts are indicative of the tone of a given review. This approach has significant limitations when we move from trying to merely evaluate whether an online opinion is positive or negative, to trying to evaluate how likely it is that the opinion will influence others. Given this issue, one promising avenue would be to evaluate the general analysis approaches utilized by opinion mining algorithms and identified in the literature in terms of how accurately they reflect how people actually interpret and are influenced by electronic online reviews. Through interviewing and a follow up survey of 136 participants, the validity of the approach in terms of ascertaining the tone of a piece of text can be evaluated, as well as the identification of measurable factors within text which make a given opinionated text more or less influential in an online context, further facilitating the development of more effective multivariate opinion mining approaches. Furthermore, the identification of factors which make an online opinion text more or less persuasive helps to facilitate the development of opinion mining approaches which can evaluate how likely a review is to affect an individual's decision making.