Comparing semantic associations in sentences and paragraphs for opinion detection in blogs
Article No.: 80, Pages 483 - 488
Abstract
Opinion Detection is one of the most interesting and challenging work in the field of Information Retrieval. Lot of research work already exists in this area with some distinctive work. A review of the reveals that researchers have been working on different levels of granularity like documents, passages, sentences and words for the task of opinion detection. In this work we revise our previous approach that combines document level heuristics with a semantic similarity based method. We evaluate this semantic similarity approach on a huge data collection using three different setups involving both sentences and passages and then compare the performance of our approach with these different setups. For evaluation purposes, we are using TREC Blog 2006 collection (148 GB) with 50 topics of TREC Blog 2006 over baseline obtained through Terrier Information System Platform. Results show that our approach improves the baseline opinion MAP by 28.89%, 30.13% and 32.26% using setup one, two and three respectively.
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Index Terms
- Comparing semantic associations in sentences and paragraphs for opinion detection in blogs
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Information & Contributors
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Published In
October 2009
525 pages
ISBN:9781605588292
DOI:10.1145/1643823
- General Chair:
- Nicolas Spyratos,
- Program Chairs:
- Epaminondas Kapetanios,
- Agma Traina,
- Publications Chairs:
- Richard Chbeir,
- Youakim Badr
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Published: 27 October 2009
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MEDES '09
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MEDES '09: The International Conference on Management of Emergent Digital EcoSystems 2009
October 27 - 30, 2009
France
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Overall Acceptance Rate 267 of 682 submissions, 39%
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