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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2312.17253 |
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| _version_ | 1866910283290640384 |
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| author | Kayed, Mohamed Díaz-Redondo, Rebeca P. Mabrouk, Alhassan |
| author_facet | Kayed, Mohamed Díaz-Redondo, Rebeca P. Mabrouk, Alhassan |
| contents | Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17253 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Learning-based Sentiment Classification: A Comparative Survey Kayed, Mohamed Díaz-Redondo, Rebeca P. Mabrouk, Alhassan Computation and Language Artificial Intelligence Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches. |
| title | Deep Learning-based Sentiment Classification: A Comparative Survey |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2312.17253 |