Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree
Today, with the enormous growth of the Internet and social networks as virtual communities and mass media, and increased use of them, a huge amount of user feedback comes from a variety of topics. Therefore, the use of novel approaches for analyzing them seems to be necessary. Text mining, as a spec...
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| Format: | Article |
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University of Qom
2020-03-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1272_36ae836b79a54fd06f4878cb628ec266.pdf |
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| _version_ | 1832577593056952320 |
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| author | Nasir Tayarani Mehrdad Jalali |
| author_facet | Nasir Tayarani Mehrdad Jalali |
| author_sort | Nasir Tayarani |
| collection | DOAJ |
| description | Today, with the enormous growth of the Internet and social networks as virtual communities and mass media, and increased use of them, a huge amount of user feedback comes from a variety of topics. Therefore, the use of novel approaches for analyzing them seems to be necessary. Text mining, as a special strategy, drives the knowledge discovery process, which uses non-verbal and attractive patterns of natural language processing. In this paper, a new hybrid approach of machine learning and vocabulary-based method to text-mining sentiment analysis on Twitter. To improve text-mining and sentiment analysis, the CART decision tree is used as a machine learning method for classification, also for extracting more precisely sentiment, we use from the list of SentiStrength algorithms as a lexicon-based method. CART is very effective in processing discrete and continuous data in text mining. The unique CART feature is a complex data structure analysis that can support regression as well as classification operations, according to the input of the problem. The ability and power of the SentiStrength algorithm to detect sentiment has also led to a thorough analysis of sentiment in tweets. The results of the implementation in the polarity recognition show improvement of classification in the most feature. |
| format | Article |
| id | doaj-art-e73628c745b1421383ee4504fbbcdcc1 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2020-03-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-e73628c745b1421383ee4504fbbcdcc12025-01-30T20:17:17ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752020-03-01619110810.22091/jemsc.2018.12721272Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision TreeNasir Tayarani0Mehrdad Jalali1MSc. Computer Engineering, Factuly of Electrical and computer Engineering, Azad Mashhad University, Mashhad, IranDepartment of Computer, Mashhad Branch, Islamic AzadUniversity, Mashhad, IranToday, with the enormous growth of the Internet and social networks as virtual communities and mass media, and increased use of them, a huge amount of user feedback comes from a variety of topics. Therefore, the use of novel approaches for analyzing them seems to be necessary. Text mining, as a special strategy, drives the knowledge discovery process, which uses non-verbal and attractive patterns of natural language processing. In this paper, a new hybrid approach of machine learning and vocabulary-based method to text-mining sentiment analysis on Twitter. To improve text-mining and sentiment analysis, the CART decision tree is used as a machine learning method for classification, also for extracting more precisely sentiment, we use from the list of SentiStrength algorithms as a lexicon-based method. CART is very effective in processing discrete and continuous data in text mining. The unique CART feature is a complex data structure analysis that can support regression as well as classification operations, according to the input of the problem. The ability and power of the SentiStrength algorithm to detect sentiment has also led to a thorough analysis of sentiment in tweets. The results of the implementation in the polarity recognition show improvement of classification in the most feature.https://jemsc.qom.ac.ir/article_1272_36ae836b79a54fd06f4878cb628ec266.pdfsocial networkstext miningcart decision treesentistrength algorithms |
| spellingShingle | Nasir Tayarani Mehrdad Jalali Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree مدیریت مهندسی و رایانش نرم social networks text mining cart decision tree sentistrength algorithms |
| title | Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree |
| title_full | Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree |
| title_fullStr | Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree |
| title_full_unstemmed | Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree |
| title_short | Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree |
| title_sort | presenting a novel hybrid approach of text mining sentiment analysis in twitter using cart decision tree |
| topic | social networks text mining cart decision tree sentistrength algorithms |
| url | https://jemsc.qom.ac.ir/article_1272_36ae836b79a54fd06f4878cb628ec266.pdf |
| work_keys_str_mv | AT nasirtayarani presentinganovelhybridapproachoftextminingsentimentanalysisintwitterusingcartdecisiontree AT mehrdadjalali presentinganovelhybridapproachoftextminingsentimentanalysisintwitterusingcartdecisiontree |