Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods
Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support system to perform stock trend predictions. This paper explores several types of feature space as different datasets for sentiment classification of t...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/14342/ http://psasir.upm.edu.my/id/eprint/14342/1/Improving%20sentiment%20classification%20accuracy%20of%20financial%20news%20using%20n-gram%20approach%20and%20feature%20weighting%20methods.pdf |
| _version_ | 1848842366421565440 |
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| author | Yazdani, Sepideh Foroozan Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina Abdul Latiff, Ahmed Razman |
| author_facet | Yazdani, Sepideh Foroozan Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina Abdul Latiff, Ahmed Razman |
| author_sort | Yazdani, Sepideh Foroozan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support system to perform stock trend predictions. This paper explores several types of feature space as different datasets for sentiment classification of the news article. Experiments are conducted based on n-gram approach (unigram, bigram and the combination of unigram and bigram) used as feature extraction with different feature weighting methods, while, document frequency (DF) is used as feature selection method. We performed experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of linear and Radial Basis Function (RBF). Results showed that an efficient feature extraction increased classification accuracy when it is used as a combination of unigram and bigram. Moreover, we also found that DF can be applied as a dimension reduction method to reduce the feature space without loss of accuracy. |
| first_indexed | 2025-11-15T07:57:59Z |
| format | Conference or Workshop Item |
| id | upm-14342 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T07:57:59Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-143422019-04-08T08:32:02Z http://psasir.upm.edu.my/id/eprint/14342/ Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods Yazdani, Sepideh Foroozan Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina Abdul Latiff, Ahmed Razman Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support system to perform stock trend predictions. This paper explores several types of feature space as different datasets for sentiment classification of the news article. Experiments are conducted based on n-gram approach (unigram, bigram and the combination of unigram and bigram) used as feature extraction with different feature weighting methods, while, document frequency (DF) is used as feature selection method. We performed experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of linear and Radial Basis Function (RBF). Results showed that an efficient feature extraction increased classification accuracy when it is used as a combination of unigram and bigram. Moreover, we also found that DF can be applied as a dimension reduction method to reduce the feature space without loss of accuracy. IEEE 2015 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/14342/1/Improving%20sentiment%20classification%20accuracy%20of%20financial%20news%20using%20n-gram%20approach%20and%20feature%20weighting%20methods.pdf Yazdani, Sepideh Foroozan and Azmi Murad, Masrah Azrifah and Mohd Sharef, Nurfadhlina and Abdul Latiff, Ahmed Razman (2015) Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods. In: 2nd International Conference on Information Science and Security (ICISS 2015), 14-16 Dec. 2015, Seoul, South Korea. . 10.1109/ICISSEC.2015.7371008 |
| spellingShingle | Yazdani, Sepideh Foroozan Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina Abdul Latiff, Ahmed Razman Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| title | Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| title_full | Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| title_fullStr | Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| title_full_unstemmed | Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| title_short | Improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| title_sort | improving sentiment classification accuracy of financial news using n-gram approach and feature weighting methods |
| url | http://psasir.upm.edu.my/id/eprint/14342/ http://psasir.upm.edu.my/id/eprint/14342/ http://psasir.upm.edu.my/id/eprint/14342/1/Improving%20sentiment%20classification%20accuracy%20of%20financial%20news%20using%20n-gram%20approach%20and%20feature%20weighting%20methods.pdf |