Chua, Hui Na and Liao, Alvin Wei Qiang and Low, Yeh Ching and Lee, Angela Siew Hoong and Ismail, Maizatul Akmar (2022) Challenges of mining twitter data for analyzing service performance: A case study of transportation service in Malaysia. In: Business Information Systems Workshops, BIS 2021, 14-17 June 2021, ELECTR Network.
Full text not available from this repository.Abstract
Literature has shown the prominence of extracting social media data to understand public opinion. However, there are little works on how these opportunities can be realized and the challenges in exploiting the opportunities in the transportation industry. Further, data quality and availability using social media may vary according to different demographics due to population size and languages used. Additionally, most of the related prior studies that show the opportunities of using social media data were conducted in North and South America. With this proposition, we seek to investigate the challenges of using Twitter data with text mining techniques for understanding users' opinions and sentiment through a case study of using the data to assess public transportation service performance specifically in a Malaysian context. Our findings indicate that social media data can only be useful in generating reasonable insights if users could input informative words for forming discussed topics to derive opinion, and incline towards a certain sentiment with adjectives. The findings also identified the need for a more proficient dictionary to classify multilingual tweets. Our research provides original evidence proving the potential of using social media data to assess public transportation services performance which may vary depending on the demographics of the social media users.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Sunway University [Grant No: CR-UM-SST-DCIS-2018-01], Sunway University [Grant No: RK004-2017], Universiti Malaya [Grant No: CR-UM-SST-DCIS-2018-01], Universiti Malaya [Grant No: RK004-2017] |
| Uncontrolled Keywords: | Text mining; Topic modelling; Latent Dirichlet Allocation (LDA); Twitter open data; Sentiment analysis; Transportation service performance |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Koh Ai Peng |
| Date Deposited: | 16 Jul 2024 02:41 |
| Last Modified: | 16 Jul 2024 07:04 |
| URI: | http://eprints.um.edu.my/id/eprint/46290 |
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