Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis
In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars...
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| Format: | Article |
| Language: | English |
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University of Tehran
2022-02-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_84895_09ce4e472aa59af08262c1bd017453a7.pdf |
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| author | Sea Yun Ying Pantea Keikhosrokiani Moussa Pourya Asl |
| author_facet | Sea Yun Ying Pantea Keikhosrokiani Moussa Pourya Asl |
| author_sort | Sea Yun Ying |
| collection | DOAJ |
| description | In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars who seek to examine privileged or unprivileged representations of certain spaces. This study aims to propose a computerized method to examine how matters of space and spatiality are addressed in literary writings. As the primary source of data, the study will focus on Viet Thanh Nguyen’s The Sympathizer (2015), which explores the lives of Vietnamese diaspora in two geographical locations, Vietnam, and America. To examine the portrayed spatial relations, that is which country is privileged over the other, and to find out the underlying opinion about the two places, this study performs topic modelling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) by using TextBlob. In addition, Python is used as the analytical tool for this project as it supports two LDA algorithms: Gensim and Mallet. To overcome the limitation that the performance of the model relies on the available libraries in Python, the study employs machine learning approach. Even though the results indicated that both geographical spaces are portrayed slightly positively, America achieves a higher polarity score than Vietnam and hence seems to be the favored space in the novel. This study can assist literary scholars in analyzing spatial relations more accurately in large volumes of works. |
| format | Article |
| id | doaj-art-de9c8689eab8414ca1c9fa2583aa8654 |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-de9c8689eab8414ca1c9fa2583aa86542025-08-20T02:21:06ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592022-02-01145th International Conference of Reliable Information and Communication Technology (IRICT 2020)16318310.22059/jitm.2022.8489584895Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment AnalysisSea Yun Ying0Pantea Keikhosrokiani1Moussa Pourya Asl2School of Computer Sciences, University Sains Malaysia, 11800 Minden, Penang, Malaysia.School of Computer Sciences, University Sains Malaysia, 11800 Minden, Penang, Malaysia.School of Humanities, University Sains Malaysia, 11800 Minden, Penang, Malaysia.In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars who seek to examine privileged or unprivileged representations of certain spaces. This study aims to propose a computerized method to examine how matters of space and spatiality are addressed in literary writings. As the primary source of data, the study will focus on Viet Thanh Nguyen’s The Sympathizer (2015), which explores the lives of Vietnamese diaspora in two geographical locations, Vietnam, and America. To examine the portrayed spatial relations, that is which country is privileged over the other, and to find out the underlying opinion about the two places, this study performs topic modelling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) by using TextBlob. In addition, Python is used as the analytical tool for this project as it supports two LDA algorithms: Gensim and Mallet. To overcome the limitation that the performance of the model relies on the available libraries in Python, the study employs machine learning approach. Even though the results indicated that both geographical spaces are portrayed slightly positively, America achieves a higher polarity score than Vietnam and hence seems to be the favored space in the novel. This study can assist literary scholars in analyzing spatial relations more accurately in large volumes of works.https://jitm.ut.ac.ir/article_84895_09ce4e472aa59af08262c1bd017453a7.pdfopinion miningsentiment analysisspatial analysisthe sympathizer |
| spellingShingle | Sea Yun Ying Pantea Keikhosrokiani Moussa Pourya Asl Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis Journal of Information Technology Management opinion mining sentiment analysis spatial analysis the sympathizer |
| title | Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis |
| title_full | Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis |
| title_fullStr | Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis |
| title_full_unstemmed | Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis |
| title_short | Opinion Mining on Viet Thanh Nguyen’s The Sympathizer Using Topic Modelling and Sentiment Analysis |
| title_sort | opinion mining on viet thanh nguyen s the sympathizer using topic modelling and sentiment analysis |
| topic | opinion mining sentiment analysis spatial analysis the sympathizer |
| url | https://jitm.ut.ac.ir/article_84895_09ce4e472aa59af08262c1bd017453a7.pdf |
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