Sentiment Analysis in Arabic Social Media Using Association Rule Mining
The fast-paced growth in worldwide webs has resulted in the development of sentiment analysis it involves the analysis of comments or web reviews. The sentiment classification of the Arabic social media is an exciting and fascinating area of study. Hence this study brings forth a new method engaging...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Medwell Journals
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37903/1/Sentiment%20Analysis%20in%20Arabic%20Social%20Media%20Using%20Association%20Rule%20Mining.pdf |
| _version_ | 1796995771941257216 |
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| author | Ahmed, AL-Saffar Bilal, Sabri Hai, Tao Suryanti, Awang Mazlina, Abdul Majid Wafaa, ALSaiagh |
| author_facet | Ahmed, AL-Saffar Bilal, Sabri Hai, Tao Suryanti, Awang Mazlina, Abdul Majid Wafaa, ALSaiagh |
| author_sort | Ahmed, AL-Saffar |
| collection | UMP |
| description | The fast-paced growth in worldwide webs has resulted in the development of sentiment analysis it involves the analysis of comments or web reviews. The sentiment classification of the Arabic social media is an exciting and fascinating area of study. Hence this study brings forth a new method engaging association rules with three Feature Selection (FS) methods in the Sentiment Analysis (SA) of web reviews in the Arabic language. The feature selection methods used are (χ2), Gini Index (GI) and Information Gain (GI). This study reveals that the use of feature selection methods has enhanced the classifier results. This means that the proposed model shows a better result than the baseline result. Finally, the experimental results show that the Chi-square Feature Selection (FS) produces the best classification technique with a high accuracy of f-measure (86.811). |
| first_indexed | 2024-03-06T13:07:11Z |
| format | Article |
| id | UMPir37903 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:07:11Z |
| publishDate | 2016 |
| publisher | Medwell Journals |
| record_format | dspace |
| spelling | UMPir379032024-01-03T06:29:37Z http://umpir.ump.edu.my/id/eprint/37903/ Sentiment Analysis in Arabic Social Media Using Association Rule Mining Ahmed, AL-Saffar Bilal, Sabri Hai, Tao Suryanti, Awang Mazlina, Abdul Majid Wafaa, ALSaiagh QA75 Electronic computers. Computer science The fast-paced growth in worldwide webs has resulted in the development of sentiment analysis it involves the analysis of comments or web reviews. The sentiment classification of the Arabic social media is an exciting and fascinating area of study. Hence this study brings forth a new method engaging association rules with three Feature Selection (FS) methods in the Sentiment Analysis (SA) of web reviews in the Arabic language. The feature selection methods used are (χ2), Gini Index (GI) and Information Gain (GI). This study reveals that the use of feature selection methods has enhanced the classifier results. This means that the proposed model shows a better result than the baseline result. Finally, the experimental results show that the Chi-square Feature Selection (FS) produces the best classification technique with a high accuracy of f-measure (86.811). Medwell Journals 2016 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37903/1/Sentiment%20Analysis%20in%20Arabic%20Social%20Media%20Using%20Association%20Rule%20Mining.pdf Ahmed, AL-Saffar and Bilal, Sabri and Hai, Tao and Suryanti, Awang and Mazlina, Abdul Majid and Wafaa, ALSaiagh (2016) Sentiment Analysis in Arabic Social Media Using Association Rule Mining. Journal of Engineering and Applied Sciences, 11 (14). pp. 3239-3247. ISSN 1816-949x (Print); 1818-7803 (Online). (Published) https://medwelljournals.com/abstract/?doi=jeasci.2016.3239.3247 |
| spellingShingle | QA75 Electronic computers. Computer science Ahmed, AL-Saffar Bilal, Sabri Hai, Tao Suryanti, Awang Mazlina, Abdul Majid Wafaa, ALSaiagh Sentiment Analysis in Arabic Social Media Using Association Rule Mining |
| title | Sentiment Analysis in Arabic Social Media Using Association Rule Mining |
| title_full | Sentiment Analysis in Arabic Social Media Using Association Rule Mining |
| title_fullStr | Sentiment Analysis in Arabic Social Media Using Association Rule Mining |
| title_full_unstemmed | Sentiment Analysis in Arabic Social Media Using Association Rule Mining |
| title_short | Sentiment Analysis in Arabic Social Media Using Association Rule Mining |
| title_sort | sentiment analysis in arabic social media using association rule mining |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/37903/1/Sentiment%20Analysis%20in%20Arabic%20Social%20Media%20Using%20Association%20Rule%20Mining.pdf |
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