Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Multidocument summarization: An added value to clustering in interactive retrieval
ACM Transactions on Information Systems (TOIS)
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Red Opal: product-feature scoring from reviews
Proceedings of the 8th ACM conference on Electronic commerce
Data mining research for customer relationship management systems: a framework and analysis
International Journal of Business Information Systems
Gather customer concerns from online product reviews - A text summarization approach
Expert Systems with Applications: An International Journal
AMAZING: A sentiment mining and retrieval system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature and Opinion Mining for Customer Review Summarization
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Application of data mining techniques for customer lifetime value parameters: a review
International Journal of Business Information Systems
Data & Knowledge Engineering
IT enabled mass customisation as a tool for bond building – an Indian case study
International Journal of Business Information Systems
| Hi-index | 0.00 |
In recent years, with the expansion of electronic commerce, the number of customer online reviews available on the internet is growing rapidly. Lots of online merchant's websites ask the customers to leave a review about their experiences with the products. The reviews gathered from these websites are rich source of information for product development and marketing. The large volume of reviews that a product receives, make it hard for a potential customer or a manufacturer to read them and know about the customers' preferences, needs and experiences. So, this large volume of text data needs to be summarised using text mining approaches. The approach used in this paper to overcome this problem, is to develop a text summarisation system which extracts and groups the representative sentences of customer reviews. The proposed system, first extracts key topics discussed frequently in the customer review texts in the form of sequences of words. Then, the proposed system, groups the sentences assigned to the key topics, based on their semantic and syntactic similarity, using a genetic clustering algorithm. The evaluation result of the proposed system shows that the technique is effective and outperforms an existing text summarisation method.