Sentiment analysis of Malaysian public health policy
Governments or any other authoritative bodies for that matter use policies as a tool to give direction to a group of people in towards a vision. Policies can come in various scales and levels, from large government policies to small district or park policies, from private bodies to public domains af...
| Main Author: | |
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2018
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| Online Access: | https://eprints.nottingham.ac.uk/53701/ |
| _version_ | 1848798974321885184 |
|---|---|
| author | Chan, Chia Yung |
| author_facet | Chan, Chia Yung |
| author_sort | Chan, Chia Yung |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Governments or any other authoritative bodies for that matter use policies as a tool to give direction to a group of people in towards a vision. Policies can come in various scales and levels, from large government policies to small district or park policies, from private bodies to public domains affecting a country’s population. While easily enacted left, right, and center, the problem lies in tracking and analyzing their performance over prolonged periods of time. Traditional methods of tracking performance with tools like surveys or observations makes the process long and tedious, inefficient, expensive, and time consuming. To begin revolutionizing tasks like this, this study serves to incorporate the latest big data technologies to be used in this context, helping speed up the process and increase efficiency in general. The study focuses on conducting a sentiment analysis of Fit Malaysia, a public health policy in Malaysia, and determining if the policy is well received by the public. It involves gathering of the public’s opinions and quickly analyzing them for feedback and keywords crucial to assess the performance of the policy, but this methodology is of course transferrable to other policies or contexts as well. Facebook was used as the social media platform to gather data, and sentiment analysis was conducted over 109 comments to discover that overall sentiments surrounding the policy is indeed positive. This was done with the Microsoft Power BI and SAS Visual Analytics software. The small sample size is amplified using the Markov chan Monte Carlo algorithm on IBM SPSS AMOS to verify that the findings from the sample are indeed representative of the population sample. |
| first_indexed | 2025-11-14T20:28:17Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-53701 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:28:17Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-537012019-02-08T11:01:44Z https://eprints.nottingham.ac.uk/53701/ Sentiment analysis of Malaysian public health policy Chan, Chia Yung Governments or any other authoritative bodies for that matter use policies as a tool to give direction to a group of people in towards a vision. Policies can come in various scales and levels, from large government policies to small district or park policies, from private bodies to public domains affecting a country’s population. While easily enacted left, right, and center, the problem lies in tracking and analyzing their performance over prolonged periods of time. Traditional methods of tracking performance with tools like surveys or observations makes the process long and tedious, inefficient, expensive, and time consuming. To begin revolutionizing tasks like this, this study serves to incorporate the latest big data technologies to be used in this context, helping speed up the process and increase efficiency in general. The study focuses on conducting a sentiment analysis of Fit Malaysia, a public health policy in Malaysia, and determining if the policy is well received by the public. It involves gathering of the public’s opinions and quickly analyzing them for feedback and keywords crucial to assess the performance of the policy, but this methodology is of course transferrable to other policies or contexts as well. Facebook was used as the social media platform to gather data, and sentiment analysis was conducted over 109 comments to discover that overall sentiments surrounding the policy is indeed positive. This was done with the Microsoft Power BI and SAS Visual Analytics software. The small sample size is amplified using the Markov chan Monte Carlo algorithm on IBM SPSS AMOS to verify that the findings from the sample are indeed representative of the population sample. 2018-02-24 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/53701/1/53701-Chan%20Chia%20Yung.pdf Chan, Chia Yung (2018) Sentiment analysis of Malaysian public health policy. [Dissertation (University of Nottingham only)] |
| spellingShingle | Chan, Chia Yung Sentiment analysis of Malaysian public health policy |
| title | Sentiment analysis of Malaysian public health policy |
| title_full | Sentiment analysis of Malaysian public health policy |
| title_fullStr | Sentiment analysis of Malaysian public health policy |
| title_full_unstemmed | Sentiment analysis of Malaysian public health policy |
| title_short | Sentiment analysis of Malaysian public health policy |
| title_sort | sentiment analysis of malaysian public health policy |
| url | https://eprints.nottingham.ac.uk/53701/ |