Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec
The discovery of an active feature extraction technique has been the focus of many researchers to improve the performance of classification methods, such as for sentiment analysis. Many of them have shown interest in using word embeddings especially Word2Vec as the features for text classification t...
| Main Authors: | , , , , |
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| Format: | Article |
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University of Malaya * Faculty of Computer Science and Information Technology
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/85796/ |
| _version_ | 1848860184529600512 |
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| author | M.Alshari, Eissa Azman, Azreen Doraisamy, Shyamala Mustapha, Norwati Alksher, Mostafa |
| author_facet | M.Alshari, Eissa Azman, Azreen Doraisamy, Shyamala Mustapha, Norwati Alksher, Mostafa |
| author_sort | M.Alshari, Eissa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The discovery of an active feature extraction technique has been the focus of many researchers to improve the performance of classification methods, such as for sentiment analysis. Many of them have shown interest in using word embeddings especially Word2Vec as the features for text classification tasks. Its ability to model high-quality distributional semantics among words has contributed to its success in many of the functions. Despite the success, Word2Vec features are high dimensional that lead to an increase in the complexity of the classifier. In this paper, an effective method for feature extraction based on Word2Vec is proposed for sentiment analysis. The process discovers polarity clusters of the terms in the vocabulary through Word2Vec and opinion lexical dictionary. The features vector for each text is constructed from the polarity clusters, which lead to a lower-dimensional vector to represent the text. This paper also investigates the effect of two opinion lexical dictionaries on the performance of sentiment analysis, and one of the dictionaries are created based on SentiWordNet. The effectiveness of the proposed method is evaluated on the IMDB with two classifiers, namely the Logistic Regression and the Support Vector Machine. The result is promising, showing that the proposed method can be more effective than the baseline approaches. |
| first_indexed | 2025-11-15T12:41:12Z |
| format | Article |
| id | upm-85796 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T12:41:12Z |
| publishDate | 2020 |
| publisher | University of Malaya * Faculty of Computer Science and Information Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-857962023-09-07T00:24:04Z http://psasir.upm.edu.my/id/eprint/85796/ Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec M.Alshari, Eissa Azman, Azreen Doraisamy, Shyamala Mustapha, Norwati Alksher, Mostafa The discovery of an active feature extraction technique has been the focus of many researchers to improve the performance of classification methods, such as for sentiment analysis. Many of them have shown interest in using word embeddings especially Word2Vec as the features for text classification tasks. Its ability to model high-quality distributional semantics among words has contributed to its success in many of the functions. Despite the success, Word2Vec features are high dimensional that lead to an increase in the complexity of the classifier. In this paper, an effective method for feature extraction based on Word2Vec is proposed for sentiment analysis. The process discovers polarity clusters of the terms in the vocabulary through Word2Vec and opinion lexical dictionary. The features vector for each text is constructed from the polarity clusters, which lead to a lower-dimensional vector to represent the text. This paper also investigates the effect of two opinion lexical dictionaries on the performance of sentiment analysis, and one of the dictionaries are created based on SentiWordNet. The effectiveness of the proposed method is evaluated on the IMDB with two classifiers, namely the Logistic Regression and the Support Vector Machine. The result is promising, showing that the proposed method can be more effective than the baseline approaches. University of Malaya * Faculty of Computer Science and Information Technology 2020 Article PeerReviewed M.Alshari, Eissa and Azman, Azreen and Doraisamy, Shyamala and Mustapha, Norwati and Alksher, Mostafa (2020) Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec. Malaysian Journal of Computer Science, 33 (3). 240 - 251. ISSN 0127-9084 https://ejournal.um.edu.my/index.php/MJCS/article/view/25280 10.22452/mjcs.vol33no3.5 |
| spellingShingle | M.Alshari, Eissa Azman, Azreen Doraisamy, Shyamala Mustapha, Norwati Alksher, Mostafa Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec |
| title | Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec |
| title_full | Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec |
| title_fullStr | Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec |
| title_full_unstemmed | Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec |
| title_short | Senti2Vec: an effective feature extraction technique for sentiment analysis based on Word2Vec |
| title_sort | senti2vec: an effective feature extraction technique for sentiment analysis based on word2vec |
| url | http://psasir.upm.edu.my/id/eprint/85796/ http://psasir.upm.edu.my/id/eprint/85796/ http://psasir.upm.edu.my/id/eprint/85796/ |