Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908280417157120 |
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| author | Shah, Mahak Hazarika, Akaash Vishal Malhotra, Meetu Patil, Sachin C. Mohanty, Joshit |
| author_facet | Shah, Mahak Hazarika, Akaash Vishal Malhotra, Meetu Patil, Sachin C. Mohanty, Joshit |
| contents | Sentiment analysis is a field within NLP that has gained importance because it is applied in various areas such as; social media surveillance, customer feedback evaluation and market research. At the same time, distributed systems allow for effective processing of large amounts of data. Therefore, this paper examines how sentiment analysis converges with distributed systems by concentrating on different approaches, challenges and future investigations. Furthermore, we do an extensive experiment where we train sentiment analysis models using both single node configuration and distributed architecture to bring out the benefits and shortcomings of each method in terms of performance and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems Shah, Mahak Hazarika, Akaash Vishal Malhotra, Meetu Patil, Sachin C. Mohanty, Joshit Computation and Language Distributed, Parallel, and Cluster Computing Machine Learning Sentiment analysis is a field within NLP that has gained importance because it is applied in various areas such as; social media surveillance, customer feedback evaluation and market research. At the same time, distributed systems allow for effective processing of large amounts of data. Therefore, this paper examines how sentiment analysis converges with distributed systems by concentrating on different approaches, challenges and future investigations. Furthermore, we do an extensive experiment where we train sentiment analysis models using both single node configuration and distributed architecture to bring out the benefits and shortcomings of each method in terms of performance and accuracy. |
| title | Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems |
| topic | Computation and Language Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2503.18260 |