Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges
Abstract Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict thei...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-04-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00342-9 |
| _version_ | 1797806430830460928 |
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| author | Ayesha Rashid Muhammad Shoaib Farooq Adnan Abid Tariq Umer Ali Kashif Bashir Yousaf Bin Zikria |
| author_facet | Ayesha Rashid Muhammad Shoaib Farooq Adnan Abid Tariq Umer Ali Kashif Bashir Yousaf Bin Zikria |
| author_sort | Ayesha Rashid |
| collection | DOAJ |
| description | Abstract Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. Similarly, six important types of data sets used for this purpose have also been discussed in this work. Lastly, future challenges and research gaps have also been presented for the researchers working in this domain. |
| first_indexed | 2024-03-13T06:07:12Z |
| format | Article |
| id | doaj.art-4f1352bd0d794cd48e7a849157791384 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-13T06:07:12Z |
| publishDate | 2021-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-4f1352bd0d794cd48e7a8491577913842023-06-11T11:29:50ZengSpringerComplex & Intelligent Systems2199-45362198-60532021-04-01932773279910.1007/s40747-021-00342-9Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesAyesha Rashid0Muhammad Shoaib Farooq1Adnan Abid2Tariq Umer3Ali Kashif Bashir4Yousaf Bin Zikria5Department of Computer Science, University of Management and TechnologyDepartment of Computer Science, University of Management and TechnologyDepartment of Computer Science, University of Management and TechnologyDepartment of Computer Science, COMSATS University IslamabadDepartment of Computing and Mathematics, Manchester Metropolitan UniversityDepartment of Information and Communication Engineering, Yeungnam UniversityAbstract Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. Similarly, six important types of data sets used for this purpose have also been discussed in this work. Lastly, future challenges and research gaps have also been presented for the researchers working in this domain.https://doi.org/10.1007/s40747-021-00342-9Intention miningIntentSearch intentQuery intentPurchase intentBehavioral intention |
| spellingShingle | Ayesha Rashid Muhammad Shoaib Farooq Adnan Abid Tariq Umer Ali Kashif Bashir Yousaf Bin Zikria Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges Complex & Intelligent Systems Intention mining Intent Search intent Query intent Purchase intent Behavioral intention |
| title | Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges |
| title_full | Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges |
| title_fullStr | Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges |
| title_full_unstemmed | Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges |
| title_short | Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges |
| title_sort | social media intention mining for sustainable information systems categories taxonomy datasets and challenges |
| topic | Intention mining Intent Search intent Query intent Purchase intent Behavioral intention |
| url | https://doi.org/10.1007/s40747-021-00342-9 |
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