Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks
This paper presents a sentiment analysis approach using Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Networks to train predictive models for sentiment analysis on social media, particularly focusing on Algerian Arabic Dialect. The method leverages word-to-vector embedding for wor...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Viçosa (UFV)
2024-10-01
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| Series: | The Journal of Engineering and Exact Sciences |
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| Online Access: | https://periodicos.ufv.br/jcec/article/view/20058 |
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| author | Abdelghani BOUZIANE Benamar BOUOUGADA Djelloul BOUCHIHA Noureddine DOUMI |
| author_facet | Abdelghani BOUZIANE Benamar BOUOUGADA Djelloul BOUCHIHA Noureddine DOUMI |
| author_sort | Abdelghani BOUZIANE |
| collection | DOAJ |
| description |
This paper presents a sentiment analysis approach using Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Networks to train predictive models for sentiment analysis on social media, particularly focusing on Algerian Arabic Dialect. The method leverages word-to-vector embedding for word representation and incorporates natural language understanding of emojis to improve semantic interpretation. The model achieves a high accuracy of 94%, demonstrating its effectiveness in analyzing sentiments in online discussions. The originality lies in applying Bi-LSTM to handle multilingual challenges on social platforms. The findings have practical implications for business, policymaking, and public sentiment evaluation, while also contributing positively to fostering informed online discourse.
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| format | Article |
| id | doaj-art-bce4b7c9d9984244a7b5fc4cbbd40d7f |
| institution | Kabale University |
| issn | 2527-1075 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Universidade Federal de Viçosa (UFV) |
| record_format | Article |
| series | The Journal of Engineering and Exact Sciences |
| spelling | doaj-art-bce4b7c9d9984244a7b5fc4cbbd40d7f2025-02-02T19:53:18ZengUniversidade Federal de Viçosa (UFV)The Journal of Engineering and Exact Sciences2527-10752024-10-0110710.18540/jcecvl10iss7pp20058Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networksAbdelghani BOUZIANE0Benamar BOUOUGADA1Djelloul BOUCHIHA2Noureddine DOUMI3Department of Computer Science, Institute of Sciences, University centre of Naama, AlgeriaDepartment of Computer Science, Institute of Sciences, University centre of Naama, AlgeriaUniversity Centre of NaamaDepartment of Computer Science, University of Saida, Algeria This paper presents a sentiment analysis approach using Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Networks to train predictive models for sentiment analysis on social media, particularly focusing on Algerian Arabic Dialect. The method leverages word-to-vector embedding for word representation and incorporates natural language understanding of emojis to improve semantic interpretation. The model achieves a high accuracy of 94%, demonstrating its effectiveness in analyzing sentiments in online discussions. The originality lies in applying Bi-LSTM to handle multilingual challenges on social platforms. The findings have practical implications for business, policymaking, and public sentiment evaluation, while also contributing positively to fostering informed online discourse. https://periodicos.ufv.br/jcec/article/view/20058Sentiment analysisArtificial intelligenceSocial Web evolutionDeep learning solutionsBi-LSTM |
| spellingShingle | Abdelghani BOUZIANE Benamar BOUOUGADA Djelloul BOUCHIHA Noureddine DOUMI Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks The Journal of Engineering and Exact Sciences Sentiment analysis Artificial intelligence Social Web evolution Deep learning solutions Bi-LSTM |
| title | Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks |
| title_full | Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks |
| title_fullStr | Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks |
| title_full_unstemmed | Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks |
| title_short | Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks |
| title_sort | sentiment analysis of algerian arabic dialect on social media using bi lstm recurrent neural networks |
| topic | Sentiment analysis Artificial intelligence Social Web evolution Deep learning solutions Bi-LSTM |
| url | https://periodicos.ufv.br/jcec/article/view/20058 |
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