PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, thro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3265 |
| _version_ | 1797615748826267648 |
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| author | Dimitrios Zaikis Nikolaos Stylianou Ioannis Vlahavas |
| author_facet | Dimitrios Zaikis Nikolaos Stylianou Ioannis Vlahavas |
| author_sort | Dimitrios Zaikis |
| collection | DOAJ |
| description | Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score. |
| first_indexed | 2024-03-11T07:30:07Z |
| format | Article |
| id | doaj.art-cac1dcfaa4d642a498220d67a30b84a2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:07Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cac1dcfaa4d642a498220d67a30b84a22023-11-17T07:21:39ZengMDPI AGApplied Sciences2076-34172023-03-01135326510.3390/app13053265PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource LanguagesDimitrios Zaikis0Nikolaos Stylianou1Ioannis Vlahavas2School of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, GreeceSchool of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, GreeceSchool of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, GreeceMedia analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score.https://www.mdpi.com/2076-3417/13/5/3265natural language processingmedia analysislow resource languageslanguage modeldomain adaption |
| spellingShingle | Dimitrios Zaikis Nikolaos Stylianou Ioannis Vlahavas PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages Applied Sciences natural language processing media analysis low resource languages language model domain adaption |
| title | PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages |
| title_full | PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages |
| title_fullStr | PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages |
| title_full_unstemmed | PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages |
| title_short | PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages |
| title_sort | pima parameter shared intelligent media analytics framework for low resource languages |
| topic | natural language processing media analysis low resource languages language model domain adaption |
| url | https://www.mdpi.com/2076-3417/13/5/3265 |
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