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Integrating Classification and Association Rule Mining
The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs), and shows that the classifier built this way is more accurate than that produced by the state-of-the-art classification system C4.5.
Sentiment Analysis and Opinion Mining
This book is a comprehensive introductory and survey text that covers all important topics and the latest developments in the field with over 400 references and is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular.
Top 10 algorithms in data mining
- Xindong Wu, Vipin Kumar, D. Steinberg
- Computer ScienceKnowledge and Information Systems
- 19 December 2007
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN,…
Opinion spam and analysis
It is shown that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques, and therefore requires some novel techniques to detect them.
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
- B. Liu
- Computer ScienceData-Centric Systems and Applications
- 1 December 2006
Liu Liu has written a comprehensive text on Web mining, which consists of two parts, where all the essential concepts and algorithms of data mining and machine learning are presented.
Mining Opinion Features in Customer Reviews
This project aims to summarize all the customer reviews of a product by mining opinion/product features that the reviewers have commented on and a number of techniques are presented to mine such features.
Opinion observer: analyzing and comparing opinions on the Web
A novel framework for analyzing and comparing consumer opinions of competing products is proposed, and a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews.
A holistic lexicon-based approach to opinion mining
This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.
Mining association rules with multiple minimum supports
This paper proposes a novel technique that allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database and shows that the technique is very effective.
Building text classifiers using positive and unlabeled examples
- B. Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip S. Yu
- Computer ScienceThird IEEE International Conference on Data…
- 19 November 2003
A more principled approach to solving the problem of building text classifiers using positive and unlabeled examples based on a biased formulation of SVM is proposed, and it is shown experimentally that it is more accurate than the existing techniques.