Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Graph drawing by force-directed placement
Software—Practice & Experience
Communications of the ACM
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Major topic detection and its application to opinion summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing (social media) networks with NodeXL
Proceedings of the fourth international conference on Communities and technologies
Fair news reader: recommending news articles with different sentiments based on user preference
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
An approach to summarizing Bengali news documents
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper the development of an opinion summarization system that works on Bengali News corpus has been described. The system identifies the sentiment information in each document, aggregates them and represents the summary information in text. The present system follows a topic-sentiment model for sentiment identification and aggregation. Topic-sentiment model is designed as discourse level theme identification and the topic-sentiment aggregation is achieved by theme clustering (k-means) and Document level Theme Relational Graph representation. The Document Level Theme Relational Graph is finally used for candidate summary sentence selection by standard page rank algorithms used in Information Retrieval (IR). As Bengali is a resource constrained language, the building of annotated gold standard corpus and acquisition of linguistics tools for lexico-syntactic, syntactic and discourse level features extraction are described in this paper. The reported accuracy of the Theme detection technique is 83.60% (precision), 76.44% (recall) and 79.85% (F-measure). The summarization system has been evaluated with Precision of 72.15%, Recall of 67.32% and F-measure of 69.65%.