Cover Image

Sarcasm detection and classification to support sentiment analysis : A study in malay social media

The classification of users' sentiment from social media data can be used to determine public opinion on certain issues. The presence of sarcasm in text may hamper the performance of sentiment analysis. This thesis presents research work conducted on sarcasm detection and classification to supp...

Full description

Saved in:
Bibliographic Details
Main Author: Mohd Suhairi Md Suhaimin
Format: Thesis
Language:English
English
Published: 2017
Subjects:
HM711-806 Groups and organizations
Online Access:https://eprints.ums.edu.my/id/eprint/37665/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/37665/2/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/37665/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ums.eprints.37665
record_format eprints
spelling my.ums.eprints.376652023-11-29T02:00:37Z https://eprints.ums.edu.my/id/eprint/37665/ Sarcasm detection and classification to support sentiment analysis : A study in malay social media Mohd Suhairi Md Suhaimin HM711-806 Groups and organizations The classification of users' sentiment from social media data can be used to determine public opinion on certain issues. The presence of sarcasm in text may hamper the performance of sentiment analysis. This thesis presents research work conducted on sarcasm detection and classification to support sentiment analysis. A Malay social media dataset, specifically focused on economic and political domain, was acquired from public comments posted on Facebook. The proposed work consists of two phases: (i) sarcasm detection and (ii) sentiment analysis with sarcasm detection and classification. In the first phase, the development of a mechanism for detecting sarcasm on bilingual data was explored. To achieve this, a feature extraction process was proposed to identify sarcasm features. Five feature categories of that can be extracted using natural language processing were considered: lexical, pragmatic, prosodic, syntactic and idiosyncratic. A non-linear Support Vector Machines classifier was employed to measure the performance of the features using the adopted evaluation metric, average Fmeasure. The best-performing features were then used as input for the second phase. In the second phase, a framework for sentiment analysis that considers sarcasm detection and classification was proposed. The framework consists of six modules, namely preprocessing, feature extraction, feature selection, sentiment classification, sarcasm detection and classification, and actual sentiment classification. Results obtained from the evaluation conducted demonstrate that the proposed features and framework are able to improve the performance of sentiment analysis. The best performance for sarcasm detection was found using a combination of syntactic, pragmatic, and prosodic features with an average F-measure score of 0.852. The best result of sentiment classification using the proposed framework, considering both sarcasm detection and classification, recorded an average F-measure score of 0. 905, outperforming the baseline sentiment classification score of 0.839. 2017 Thesis NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/37665/1/24%20PAGES.pdf text en https://eprints.ums.edu.my/id/eprint/37665/2/FULLTEXT.pdf Mohd Suhairi Md Suhaimin (2017) Sarcasm detection and classification to support sentiment analysis : A study in malay social media. Masters thesis, Universiti Malaysia Sabah.
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic HM711-806 Groups and organizations
spellingShingle HM711-806 Groups and organizations
Mohd Suhairi Md Suhaimin
Sarcasm detection and classification to support sentiment analysis : A study in malay social media
description The classification of users' sentiment from social media data can be used to determine public opinion on certain issues. The presence of sarcasm in text may hamper the performance of sentiment analysis. This thesis presents research work conducted on sarcasm detection and classification to support sentiment analysis. A Malay social media dataset, specifically focused on economic and political domain, was acquired from public comments posted on Facebook. The proposed work consists of two phases: (i) sarcasm detection and (ii) sentiment analysis with sarcasm detection and classification. In the first phase, the development of a mechanism for detecting sarcasm on bilingual data was explored. To achieve this, a feature extraction process was proposed to identify sarcasm features. Five feature categories of that can be extracted using natural language processing were considered: lexical, pragmatic, prosodic, syntactic and idiosyncratic. A non-linear Support Vector Machines classifier was employed to measure the performance of the features using the adopted evaluation metric, average Fmeasure. The best-performing features were then used as input for the second phase. In the second phase, a framework for sentiment analysis that considers sarcasm detection and classification was proposed. The framework consists of six modules, namely preprocessing, feature extraction, feature selection, sentiment classification, sarcasm detection and classification, and actual sentiment classification. Results obtained from the evaluation conducted demonstrate that the proposed features and framework are able to improve the performance of sentiment analysis. The best performance for sarcasm detection was found using a combination of syntactic, pragmatic, and prosodic features with an average F-measure score of 0.852. The best result of sentiment classification using the proposed framework, considering both sarcasm detection and classification, recorded an average F-measure score of 0. 905, outperforming the baseline sentiment classification score of 0.839.
format Thesis
author Mohd Suhairi Md Suhaimin
author_facet Mohd Suhairi Md Suhaimin
author_sort Mohd Suhairi Md Suhaimin
title Sarcasm detection and classification to support sentiment analysis : A study in malay social media
title_short Sarcasm detection and classification to support sentiment analysis : A study in malay social media
title_full Sarcasm detection and classification to support sentiment analysis : A study in malay social media
title_fullStr Sarcasm detection and classification to support sentiment analysis : A study in malay social media
title_full_unstemmed Sarcasm detection and classification to support sentiment analysis : A study in malay social media
title_sort sarcasm detection and classification to support sentiment analysis : a study in malay social media
publishDate 2017
url https://eprints.ums.edu.my/id/eprint/37665/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/37665/2/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/37665/
_version_ 1783877957807243264
score 13.211869

Similar Items