An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis
Sentiment analysis is a form of opinion mining technique that identifies the polarity of extracted opinions. Nowadays, opinion mining has become an important research area in recent decades to identify the polarity of the statements. Various research works have been carried out on sentiment analysis...
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University of Belgrade
2024-01-01
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| Series: | Yugoslav Journal of Operations Research |
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| Online Access: | https://doiserbia.nb.rs/img/doi/0354-0243/2024/0354-02432400034A.pdf |
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| author | Anuradha K. Mallik Banitamani Krishna Vamsi M. |
| author_facet | Anuradha K. Mallik Banitamani Krishna Vamsi M. |
| author_sort | Anuradha K. |
| collection | DOAJ |
| description | Sentiment analysis is a form of opinion mining technique that identifies the polarity of extracted opinions. Nowadays, opinion mining has become an important research area in recent decades to identify the polarity of the statements. Various research works have been carried out on sentiment analysis. However, the existing sentimental analysis techniques, such as time and space complexity, still have considerable limitations. To deal with these issues, this paper proposed the Cucconi Feature Extracted Random Decision Forest Classification (CFDFC) Approach. The main objective of the CFDFC approach is to provide effective sentiment analysis with improved accuracy and reduced time complexity. The proposed CFDFC framework comprisespre-processing, feature extraction, and classification. The pre-processing step eliminates stop words and stem words from user reviews. After the pre-processing step, the feature extraction process is carried out to minimize the dimensionality and time consumption for opinion classification. Cucconi's projective feature extraction process is used in this work to reduce dimensionality. Finally, the classification process is formulated using a random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements. The performance evaluation of the proposed approach is carried out using performance metrics such as accuracy, error rates, recall values, and time and space complexities concerning the number of review statements gathered from the dataset. The results show that the proposed CFDFC model achieves remarkable accuracy, recall, and minimal time complexity compared to existing methods. |
| format | Article |
| id | doaj-art-05c2bec17dcb400c91980202a641def8 |
| institution | Kabale University |
| issn | 0354-0243 1820-743X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Belgrade |
| record_format | Article |
| series | Yugoslav Journal of Operations Research |
| spelling | doaj-art-05c2bec17dcb400c91980202a641def82025-01-30T06:47:14ZengUniversity of BelgradeYugoslav Journal of Operations Research0354-02431820-743X2024-01-0134476578310.2298/YJOR240315034A0354-02432400034AAn efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysisAnuradha K.0https://orcid.org/0000-0002-9004-5792Mallik Banitamani1https://orcid.org/0000-0003-0912-3288Krishna Vamsi M.2https://orcid.org/0000-0001-5285-9990Department of CSE, Centurion University of Technology & Management, Odisha and Assistant Professor, Dr. L. B. College of Engineering for women, Visakhapatnam, IndiaDepartment of Mathematics, Centurion University of Technology & Management, Odisha, IndiaDepartment of CSE, Aditya Engineering College, Surampalem, IndiaSentiment analysis is a form of opinion mining technique that identifies the polarity of extracted opinions. Nowadays, opinion mining has become an important research area in recent decades to identify the polarity of the statements. Various research works have been carried out on sentiment analysis. However, the existing sentimental analysis techniques, such as time and space complexity, still have considerable limitations. To deal with these issues, this paper proposed the Cucconi Feature Extracted Random Decision Forest Classification (CFDFC) Approach. The main objective of the CFDFC approach is to provide effective sentiment analysis with improved accuracy and reduced time complexity. The proposed CFDFC framework comprisespre-processing, feature extraction, and classification. The pre-processing step eliminates stop words and stem words from user reviews. After the pre-processing step, the feature extraction process is carried out to minimize the dimensionality and time consumption for opinion classification. Cucconi's projective feature extraction process is used in this work to reduce dimensionality. Finally, the classification process is formulated using a random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements. The performance evaluation of the proposed approach is carried out using performance metrics such as accuracy, error rates, recall values, and time and space complexities concerning the number of review statements gathered from the dataset. The results show that the proposed CFDFC model achieves remarkable accuracy, recall, and minimal time complexity compared to existing methods.https://doiserbia.nb.rs/img/doi/0354-0243/2024/0354-02432400034A.pdfsentiment analysisreview statementsweak learnerrandom decision forest classifierid3 decision treecucconi projective feature extraction |
| spellingShingle | Anuradha K. Mallik Banitamani Krishna Vamsi M. An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis Yugoslav Journal of Operations Research sentiment analysis review statements weak learner random decision forest classifier id3 decision tree cucconi projective feature extraction |
| title | An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis |
| title_full | An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis |
| title_fullStr | An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis |
| title_full_unstemmed | An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis |
| title_short | An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis |
| title_sort | efficient cucconi based feature extraction with random decision forest classification for improved sentiment analysis |
| topic | sentiment analysis review statements weak learner random decision forest classifier id3 decision tree cucconi projective feature extraction |
| url | https://doiserbia.nb.rs/img/doi/0354-0243/2024/0354-02432400034A.pdf |
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