Mining in social media data : happiness forecast @ SG
Individual happiness in each region play a significant role for social metric. Happiness has often indirectly characterized and overshadowed by social media indicators. This project studies a methodology to measure the correlation between the real time expressions of individuals made across Singapor...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/73961 |
| _version_ | 1811682837397504000 |
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| author | Tan, Poh Lian |
| author2 | Kong Wai-Kin Adams |
| author_facet | Kong Wai-Kin Adams Tan, Poh Lian |
| author_sort | Tan, Poh Lian |
| collection | NTU |
| description | Individual happiness in each region play a significant role for social metric. Happiness has often indirectly characterized and overshadowed by social media indicators. This project studies a methodology to measure the correlation between the real time expressions of individuals made across Singapore and range of social phenomena factors- population, dengue cluster and electricity consumption. We will examine the expression made on the social media -Twitter and uncover the happiness index over different regions. A total of 10,000 raw data in Twitter was collected which consists of users share thoughts, images, links for all the regions in Singapore. The collection of real-time tweets is customised to suit our project by using streaming API in Python. The next stage is to perform text-mining techniques to obtain the meaningful term. After data cleaning and pre-processing phrase, the parsed term will be tagged to a happiness index dictionary to compute the happiness scores (H-Score). Additionally, happiness index of singlish tokens will be further classified with Sentic API. Finally, we will be evaluating the relationships between the happiness scores and the real-world phenomena. |
| first_indexed | 2024-10-01T04:03:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/73961 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:03:11Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/739612023-03-03T20:33:43Z Mining in social media data : happiness forecast @ SG Tan, Poh Lian Kong Wai-Kin Adams School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Individual happiness in each region play a significant role for social metric. Happiness has often indirectly characterized and overshadowed by social media indicators. This project studies a methodology to measure the correlation between the real time expressions of individuals made across Singapore and range of social phenomena factors- population, dengue cluster and electricity consumption. We will examine the expression made on the social media -Twitter and uncover the happiness index over different regions. A total of 10,000 raw data in Twitter was collected which consists of users share thoughts, images, links for all the regions in Singapore. The collection of real-time tweets is customised to suit our project by using streaming API in Python. The next stage is to perform text-mining techniques to obtain the meaningful term. After data cleaning and pre-processing phrase, the parsed term will be tagged to a happiness index dictionary to compute the happiness scores (H-Score). Additionally, happiness index of singlish tokens will be further classified with Sentic API. Finally, we will be evaluating the relationships between the happiness scores and the real-world phenomena. Bachelor of Engineering (Computer Science) 2018-04-23T02:41:13Z 2018-04-23T02:41:13Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/73961 en Nanyang Technological University 74 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Tan, Poh Lian Mining in social media data : happiness forecast @ SG |
| title | Mining in social media data : happiness forecast @ SG |
| title_full | Mining in social media data : happiness forecast @ SG |
| title_fullStr | Mining in social media data : happiness forecast @ SG |
| title_full_unstemmed | Mining in social media data : happiness forecast @ SG |
| title_short | Mining in social media data : happiness forecast @ SG |
| title_sort | mining in social media data happiness forecast sg |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/73961 |
| work_keys_str_mv | AT tanpohlian mininginsocialmediadatahappinessforecastsg |