A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909087106596864 |
|---|---|
| author | Elahi, Kazi Toufique Rahman, Tasnuva Binte Shahriar, Shakil Sarker, Samir Shawon, Md. Tanvir Rouf Shahariar, G. M. |
| author_facet | Elahi, Kazi Toufique Rahman, Tasnuva Binte Shahriar, Shakil Sarker, Samir Shawon, Md. Tanvir Rouf Shahariar, G. M. |
| contents | While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14360 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts Elahi, Kazi Toufique Rahman, Tasnuva Binte Shahriar, Shakil Sarker, Samir Shawon, Md. Tanvir Rouf Shahariar, G. M. Computation and Language 68T50 (Primary) I.2.7 While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts |
| title | A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts |
| topic | Computation and Language 68T50 (Primary) I.2.7 |
| url | https://arxiv.org/abs/2401.14360 |