Daoud M. Daoud
ABSTRACT
In this paper, we describe a procedure for extracting annotated Arabic negative and positive tweets. We use these extracted annotated tweets to build our sentiment system using Naive Bayes with TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We automatically collect 200 thousand annotated tweets. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.
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Digital Library
- D. Daoud, A. Al-kouz, K. Hasssan, and L. Milliam, "Arabic Tweets Clustering and Labeling Based on Lingual and Semantically Enriched Bayesian Network Model," Recent Patents on Computer Science, vol. 8, pp. 1--14, 2015.Google ScholarCross Ref
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- L. Albraheem and H. S. Al-Khalifa, "Exploring the problems of sentiment analysis in informal Arabic," presented at Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services. Google ScholarDigital Library
- S. R. El-Beltagy and A. Ali, "Open issues in the sentiment analysis of Arabic social media: A case study," presented at Innovations in information technology (iit), 2013 9th international conference on.Google Scholar
- A. N. A., M. N. A., S. M., Al-Ayyoub M., and "Arabic Sentiment Analysis: Corpus-based and Lexicon-based," presented at IEEE Conference on Applied Electrical Engineering and Computing Technologies (AEECT 2013), Amman, Jordan., 2013.Google Scholar
Index Terms
- Building a Sentiment Analysis system using automatically generated training Dataset
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