Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA
Lexical sentiment analysis (LSA) underlines a family of methods combining natural language processing, machine learning, or graph navigation techniques to identify the underlying sentiments or emotions carried in textual data. In this paper, we introduce LISA, an unsupervised word-level knowledge gr...
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2019
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| Online Access: | http://hdl.handle.net/10725/16275 https://doi.org/10.1109/ICCC.2019.00008 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/document/8816968 |
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| _version_ | 1864513472531791872 |
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| author | Fares, Mireille |
| author2 | Moufarrej, Angela Jreij, Eliane Tekli, Joe Grosky, William |
| author2_role | author author author author |
| author_facet | Fares, Mireille Moufarrej, Angela Jreij, Eliane Tekli, Joe Grosky, William |
| author_role | author |
| dc.contributor.none.fl_str_mv | Bertino, Elisa |
| dc.creator.none.fl_str_mv | Fares, Mireille Moufarrej, Angela Jreij, Eliane Tekli, Joe Grosky, William |
| dc.date.none.fl_str_mv | 2019 2019-08-29 2024-11-05T07:51:49Z 2024-11-05T07:51:49Z |
| dc.identifier.none.fl_str_mv | 9781728127118 http://hdl.handle.net/10725/16275 https://doi.org/10.1109/ICCC.2019.00008 Fares, M., Moufarrej, A., Jreij, E., Tekli, J., & Grosky, W. (2019, July). Difficulties and improvements to graph-based lexical sentiment analysis using LISA. In 2019 IEEE International Conference on Cognitive Computing (ICCC) (pp. 28-35). IEEE. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/document/8816968 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cognitive science -- Data processing -- Congresses Computational intelligence -- Congresses |
| dc.title.none.fl_str_mv | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | Lexical sentiment analysis (LSA) underlines a family of methods combining natural language processing, machine learning, or graph navigation techniques to identify the underlying sentiments or emotions carried in textual data. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LexIcal Sentiment Analysis framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-affective graph (LAG), created by connecting a typical lexical knowledgebase (KB) like WordNet, with a reliable affect KB like WordNet-Affect Hierarchy. LISA was designed in two consecutive iterations, producing two main modules: i) LISA 1.0 for affect navigation, and ii) LISA 2.0 for affect propagation and lookup. LISA 1.0 suffered from the semantic connectivity problem shared by some existing lexicon-based methods, and required polynomial execution time. This led to the development of LISA 2.0, which i) processes affective relationships separately from lexical/semantic connections (solving the semantic connectivity problem of LISA 1.0), and ii) produces a sentiment lexicon which can be searched in logarithmic time (handling LISA 1.0's efficiency problem). Experimental results on the ANEW dataset show that LISA 2.0, while completely unsupervised, is on a par with existing supervised solutions, highlighting its quality and potential. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_92e3cfe2d9f111543169ff2f806cdbeb |
| identifier_str_mv | 9781728127118 Fares, M., Moufarrej, A., Jreij, E., Tekli, J., & Grosky, W. (2019, July). Difficulties and improvements to graph-based lexical sentiment analysis using LISA. In 2019 IEEE International Conference on Cognitive Computing (ICCC) (pp. 28-35). IEEE. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/16275 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISAFares, MireilleMoufarrej, AngelaJreij, ElianeTekli, JoeGrosky, WilliamCognitive science -- Data processing -- CongressesComputational intelligence -- CongressesLexical sentiment analysis (LSA) underlines a family of methods combining natural language processing, machine learning, or graph navigation techniques to identify the underlying sentiments or emotions carried in textual data. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LexIcal Sentiment Analysis framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-affective graph (LAG), created by connecting a typical lexical knowledgebase (KB) like WordNet, with a reliable affect KB like WordNet-Affect Hierarchy. LISA was designed in two consecutive iterations, producing two main modules: i) LISA 1.0 for affect navigation, and ii) LISA 2.0 for affect propagation and lookup. LISA 1.0 suffered from the semantic connectivity problem shared by some existing lexicon-based methods, and required polynomial execution time. This led to the development of LISA 2.0, which i) processes affective relationships separately from lexical/semantic connections (solving the semantic connectivity problem of LISA 1.0), and ii) produces a sentiment lexicon which can be searched in logarithmic time (handling LISA 1.0's efficiency problem). Experimental results on the ANEW dataset show that LISA 2.0, while completely unsupervised, is on a par with existing supervised solutions, highlighting its quality and potential.1 online resource (xviii, 131 pages) : illustrations (some color)Includes bibliographical references.IEEEBertino, Elisa2024-11-05T07:51:49Z2024-11-05T07:51:49Z20192019-08-29Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9781728127118http://hdl.handle.net/10725/16275https://doi.org/10.1109/ICCC.2019.00008Fares, M., Moufarrej, A., Jreij, E., Tekli, J., & Grosky, W. (2019, July). Difficulties and improvements to graph-based lexical sentiment analysis using LISA. In 2019 IEEE International Conference on Cognitive Computing (ICCC) (pp. 28-35). IEEE.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/document/8816968eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/162752024-11-05T07:51:49Z |
| spellingShingle | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA Fares, Mireille Cognitive science -- Data processing -- Congresses Computational intelligence -- Congresses |
| status_str | publishedVersion |
| title | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| title_full | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| title_fullStr | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| title_full_unstemmed | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| title_short | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| title_sort | Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA |
| topic | Cognitive science -- Data processing -- Congresses Computational intelligence -- Congresses |
| url | http://hdl.handle.net/10725/16275 https://doi.org/10.1109/ICCC.2019.00008 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/document/8816968 |