Fine-grained sentiment classification of social media data
Social media offers a rich source of information, such as critiques, feedbacks, and other opinions posted online by internet users. Such information may reflect attitudes and sentiments of users towards certain topics, products, or services. The need to interpret the huge amount of data available on...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/71028 |
| _version_ | 1826112460556337152 |
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| author | She, Yanyao |
| author2 | Lin Zhiping |
| author_facet | Lin Zhiping She, Yanyao |
| author_sort | She, Yanyao |
| collection | NTU |
| description | Social media offers a rich source of information, such as critiques, feedbacks, and other opinions posted online by internet users. Such information may reflect attitudes and sentiments of users towards certain topics, products, or services. The need to interpret the huge amount of data available on social media has accelerated the emergence of sentiment analysis.
By definition, Sentiment analysis, or opinion mining, is a set of techniques under Natural Language Processing (NLP) that helps to identify users’ sentiments mainly by investigating, extracting and analysing subjective texts. In this project, the student is required to conduct research on academic literature as well as existing applications with an objective of improving the performance of SentiMo, a proprietary sentiment analysis engine developed by IHPC.
This report consists of two parts. The first part includes the findings of a holistic research on sentiment analysis and existing applications in the market, as well as four improvement recommendations proposed for SentiMo as a result. Afterwards, the second part of the report is focused on the field of sarcasm detection for social media data, which aims at identifying sarcasm in users’ posts. A multidimensional analysis of sarcasm on social media and a comprehensive rule-based sarcasm detection framework are presented. |
| first_indexed | 2024-10-01T03:07:23Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/71028 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:07:23Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/710282023-07-07T17:04:24Z Fine-grained sentiment classification of social media data She, Yanyao Lin Zhiping School of Electrical and Electronic Engineering A*STAR Institute of High Performance Computing DRNTU::Engineering::Electrical and electronic engineering Social media offers a rich source of information, such as critiques, feedbacks, and other opinions posted online by internet users. Such information may reflect attitudes and sentiments of users towards certain topics, products, or services. The need to interpret the huge amount of data available on social media has accelerated the emergence of sentiment analysis. By definition, Sentiment analysis, or opinion mining, is a set of techniques under Natural Language Processing (NLP) that helps to identify users’ sentiments mainly by investigating, extracting and analysing subjective texts. In this project, the student is required to conduct research on academic literature as well as existing applications with an objective of improving the performance of SentiMo, a proprietary sentiment analysis engine developed by IHPC. This report consists of two parts. The first part includes the findings of a holistic research on sentiment analysis and existing applications in the market, as well as four improvement recommendations proposed for SentiMo as a result. Afterwards, the second part of the report is focused on the field of sarcasm detection for social media data, which aims at identifying sarcasm in users’ posts. A multidimensional analysis of sarcasm on social media and a comprehensive rule-based sarcasm detection framework are presented. Bachelor of Engineering 2017-05-12T08:39:39Z 2017-05-12T08:39:39Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71028 en Nanyang Technological University 52 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering She, Yanyao Fine-grained sentiment classification of social media data |
| title | Fine-grained sentiment classification of social media data |
| title_full | Fine-grained sentiment classification of social media data |
| title_fullStr | Fine-grained sentiment classification of social media data |
| title_full_unstemmed | Fine-grained sentiment classification of social media data |
| title_short | Fine-grained sentiment classification of social media data |
| title_sort | fine grained sentiment classification of social media data |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/71028 |
| work_keys_str_mv | AT sheyanyao finegrainedsentimentclassificationofsocialmediadata |