User stance prediction
Stance detection is defined as understanding a person's view and opinion towards a given proposition. A person can be supporting, opposing or neutral towards a proposition. The stance detection problem consists of two sub-tasks, namely stance classification and stance prediction. This dissert...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166230 |
| _version_ | 1826116147146129408 |
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| author | Gan, Kah Ee |
| author2 | Smitha Kavallur Pisharath Gopi |
| author_facet | Smitha Kavallur Pisharath Gopi Gan, Kah Ee |
| author_sort | Gan, Kah Ee |
| collection | NTU |
| description | Stance detection is defined as understanding a person's view and opinion towards a given proposition. A person can be supporting, opposing or neutral towards a proposition. The stance detection problem consists of two sub-tasks, namely stance classification and stance prediction.
This dissertation will be an extension of a work done previously during my internship at Defence Science and Technology Agency, Singapore (DSTA) on stance classification. We will be extending this project to the other sub-task of stance detection, which is stance prediction. The stance prediction's main objective is to identify the stance that towards an event has not occurred yet, or is a topic that a target user's or a target group of users' have not mentioned yet based on the past texts (tweets, posts, articles, comments, etc.) that is written by them. This Final Year Project (FYP) will explore the extensiveness of our current approach on user stance prediction as well as compare its performance with another approach using a hybrid collaborative filtering framework on 2 datasets, the VAST dataset and a self-curated r/singapore Reddit dataset. We will also be performing holistic evaluations to explore their respective abilities and limitations.
This study is crucial for Natural Language Processing (NLP) researchers to design more comprehensive and accurate predictors, potentially extending their capabilities to other classification tasks. |
| first_indexed | 2024-10-01T04:06:35Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166230 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:35Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1662302023-05-12T15:36:49Z User stance prediction Gan, Kah Ee Smitha Kavallur Pisharath Gopi School of Computer Science and Engineering Defence Science and Technology Agency, Singapore smitha@ntu.edu.sg Engineering::Electrical and electronic engineering Stance detection is defined as understanding a person's view and opinion towards a given proposition. A person can be supporting, opposing or neutral towards a proposition. The stance detection problem consists of two sub-tasks, namely stance classification and stance prediction. This dissertation will be an extension of a work done previously during my internship at Defence Science and Technology Agency, Singapore (DSTA) on stance classification. We will be extending this project to the other sub-task of stance detection, which is stance prediction. The stance prediction's main objective is to identify the stance that towards an event has not occurred yet, or is a topic that a target user's or a target group of users' have not mentioned yet based on the past texts (tweets, posts, articles, comments, etc.) that is written by them. This Final Year Project (FYP) will explore the extensiveness of our current approach on user stance prediction as well as compare its performance with another approach using a hybrid collaborative filtering framework on 2 datasets, the VAST dataset and a self-curated r/singapore Reddit dataset. We will also be performing holistic evaluations to explore their respective abilities and limitations. This study is crucial for Natural Language Processing (NLP) researchers to design more comprehensive and accurate predictors, potentially extending their capabilities to other classification tasks. Bachelor of Science in Data Science and Artificial Intelligence 2023-05-09T00:19:46Z 2023-05-09T00:19:46Z 2023 Final Year Project (FYP) Gan, K. E. (2023). User stance prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166230 https://hdl.handle.net/10356/166230 en SCSE22-0636 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Gan, Kah Ee User stance prediction |
| title | User stance prediction |
| title_full | User stance prediction |
| title_fullStr | User stance prediction |
| title_full_unstemmed | User stance prediction |
| title_short | User stance prediction |
| title_sort | user stance prediction |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/166230 |
| work_keys_str_mv | AT gankahee userstanceprediction |