  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
