Deriving marketing intelligence from online discussion
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
Business and competitive analysis: effective application of new and classic methods
Business and competitive analysis: effective application of new and classic methods
Timeline Analysis of Web News Events
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining the change of event trends for decision support in environmental scanning
Expert Systems with Applications: An International Journal
Leveraging Sentiment Analysis for Topic Detection
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
WaterCooler: exploring an organization through enterprise social media
Proceedings of the ACM 2009 international conference on Supporting group work
Topic-dependent sentiment analysis of financial blogs
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Sentiment Mining within Social Media for Topic Identification
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
MOETA: a novel text-mining model for collecting and analysing competitive intelligence
International Journal of Advanced Media and Communication
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With the development of social media, the business environment has become more complex and dynamic than ever before. Effective and prompt analysis of social media content provides a business with vital competitive power. In order to maximize the strategic function of social media, we integrate a competitive intelligence analysis method, event timeline analysis, with natural language processing technologies (sentiment analysis, entity and event extraction) and information visualization. This results in a novel social media analysis model, SoMEST. We demonstrate the use of the model with a practical example.