Automatic extraction and classification approach of opinions in texts

In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company "SemEva...

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Bibliographic Details
Published in2010 10th International Conference on Intelligent Systems Design and Applications pp. 918 - 922
Main Authors Bouchlaghem, Rihab, Elkhlifi, Aymen, Faiz, Rim
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2010
Subjects
Classification algorithms
Intelligent systems
Machine learning
Natural Language Processing
Opinion Mining
Prediction algorithms
Presses
Semantic Similarity
Semantics
Tagging
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ISBN1424481341
9781424481347
ISSN2164-7143
DOI10.1109/ISDA.2010.5687072

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Summary:In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company "SemEval 2007" corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 % compared to others participants.
ISBN:1424481341
9781424481347
ISSN:2164-7143
DOI:10.1109/ISDA.2010.5687072