Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation
Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and clas...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8892552 |
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| _version_ | 1832550097526718464 |
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| author | Asad Masood Khattak Rabia Batool Fahad Ahmed Satti Jamil Hussain Wajahat Ali Khan Adil Mehmood Khan Bashir Hayat |
| author_facet | Asad Masood Khattak Rabia Batool Fahad Ahmed Satti Jamil Hussain Wajahat Ali Khan Adil Mehmood Khan Bashir Hayat |
| author_sort | Asad Masood Khattak |
| collection | DOAJ |
| description | Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately. |
| format | Article |
| id | doaj-art-bdbe395d6bf04814ba99541c4c3507c0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-bdbe395d6bf04814ba99541c4c3507c02025-02-03T06:07:41ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88925528892552Tweets Classification and Sentiment Analysis for Personalized Tweets RecommendationAsad Masood Khattak0Rabia Batool1Fahad Ahmed Satti2Jamil Hussain3Wajahat Ali Khan4Adil Mehmood Khan5Bashir Hayat6College of Technological Innovation, Zayed University, Dubai, UAECollege of Technological Innovation, Zayed University, Dubai, UAEDepartment of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of KoreaDepartment of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of KoreaCollege of Engineering and Technology, University of Derby, Markeaton Street, Derby DE223AW, UKInstitute of Information Systems, Innopolis University, Innopolis, RussiaInstitute of Management Sciences, Peshawar, PakistanMining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.http://dx.doi.org/10.1155/2020/8892552 |
| spellingShingle | Asad Masood Khattak Rabia Batool Fahad Ahmed Satti Jamil Hussain Wajahat Ali Khan Adil Mehmood Khan Bashir Hayat Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation Complexity |
| title | Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation |
| title_full | Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation |
| title_fullStr | Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation |
| title_full_unstemmed | Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation |
| title_short | Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation |
| title_sort | tweets classification and sentiment analysis for personalized tweets recommendation |
| url | http://dx.doi.org/10.1155/2020/8892552 |
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