Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis
Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dim...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2018/8909357 |
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| author | Monalisa Ghosh Goutam Sanyal |
| author_facet | Monalisa Ghosh Goutam Sanyal |
| author_sort | Monalisa Ghosh |
| collection | DOAJ |
| description | Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy. |
| format | Article |
| id | doaj-art-bd58a9fb8e9d452ab420417451d3af8a |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-bd58a9fb8e9d452ab420417451d3af8a2025-02-03T01:20:03ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322018-01-01201810.1155/2018/89093578909357Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment AnalysisMonalisa Ghosh0Goutam Sanyal1Department of Computer Science and Engineering National Institute of Technology, Durgapur, West Bengal, IndiaDepartment of Computer Science and Engineering National Institute of Technology, Durgapur, West Bengal, IndiaSentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy.http://dx.doi.org/10.1155/2018/8909357 |
| spellingShingle | Monalisa Ghosh Goutam Sanyal Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis Applied Computational Intelligence and Soft Computing |
| title | Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis |
| title_full | Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis |
| title_fullStr | Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis |
| title_full_unstemmed | Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis |
| title_short | Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis |
| title_sort | performance assessment of multiple classifiers based on ensemble feature selection scheme for sentiment analysis |
| url | http://dx.doi.org/10.1155/2018/8909357 |
| work_keys_str_mv | AT monalisaghosh performanceassessmentofmultipleclassifiersbasedonensemblefeatureselectionschemeforsentimentanalysis AT goutamsanyal performanceassessmentofmultipleclassifiersbasedonensemblefeatureselectionschemeforsentimentanalysis |