# Exploring Large Language Model and Machine Learning Models for Sentiment Analysis of Bengali Natok Reviews

Abu Sa Adat Mohamed Moonim Al Ahsan, Md Tofazzal Hossain Rasel, Maher Ali Rusho, Md. Mahir Jawad, Shirajum Munira Mow, Md. Raihan Rahman, Md Ashfaqul Azam Chowdhury, Md Khurshid Jahan, Mithila Arman

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## To cite this version:

Abu Sa Adat Mohamed Moonim Al Ahsan, Md Tofazzal Hossain Rasel, Maher Ali Rusho, Md. Mahir Jawad, Shirajum Munira Mow, et al.. Exploring Large Language Model and Machine Learning Models for Sentiment Analysis of Bengali Natok Reviews. 5th International Conference on Electrical, Computer and Energy Technologies (ICECET 2025), Jul 2025, Paris, France. hal-05068203

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# Exploring Large Language Model and Machine Learning Models for Sentiment Analysis of Bengali Natok Reviews

1st Abu Sa Adat Mohamed Moonim Al Ahsan

Dept. of Computer Science & Engineering
BRAC University
Dhaka, Bangladesh
abu.sa.adat.mohamed.moon.im.al.ahshan@g.bracu.ac.bd

3rd Maher Ali Rusho*

Senior Scientist
Department of Computational Material & Data Analytics
Mr. R Business Corporation (NGO)
Chennai, Tamil Nadu, India
maher.rusho@colorado.edu

5th Shirajum Munira Mow

Dept. of Computer Science & Engineering
Jahangirnagar University
Dhaka, Bangladesh
shirajummunira24@gmail.com

7th Md Ashfaqul Azam Chowdhury

Dept. of Computer Science & Engineering
Ahsanullah University of Science
& Technology
Dhaka, Bangladesh
azamashfaqul12@gmail.com

9th Mithila Arman

Dept. of Computer Science & Engineering
BRAC University
Dhaka, Bangladesh
mithila.arman@g.bracu.ac.bd

2nd Md Tofazzal Hossain Rasel

Dept. of Computer Science & Engineering
ESILV-Leonard de Vinci Graduate School of Engineering
Pole Leonard de Vinci
Courbevoie, France
md-tofazzal-hossain.rasel@edu.devinci.fr

4th Md. Mahir Jawad

Dept. of Computer Science & Engineering
BRAC University
Dhaka, Bangladesh
md.mahir.jawad@g.bracu.ac.bd

6th MD. Raihan Rahman

Dept. of Computer Science & Engineering
BRAC University
Dhaka, Bangladesh
raihanraphy@gmail.com

8th Md. Khurshid Jahan

Dept. of Electrical & Computer Engineering
North South University
Dhaka, Bangladesh
khurshid.jahan@northsouth.edu

**Abstract**—Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing (NLP) tasks, including sentiment analysis and text classification. However, applying these models to low-resource languages like Bengali remains challenging due to the limited availability of high-quality annotated datasets. This study explores the effectiveness of LLMs in Bengali sentiment analysis by evaluating five pre-trained transformer-based models, including BanglaBERT, BERT, and Llama 3.1 against ten traditional machine learning models. We utilized the Bengali Review Dataset, sourced from Kaggle, to conduct a comprehensive performance comparison. To ensure a fair evaluation, we preprocess the dataset using tokenization and augmentation techniques to enhance data quality and mitigate class imbalance. Models are trained and tested

using standard evaluation metrics such as accuracy, F1-score, and confusion matrices. Our results highlight the advantages and limitations of both traditional and deep learning-based approaches, offering insights into the suitability of LLMs for Bengali sentiment classification. BanglaBERT achieves the highest accuracy of 0.97 and BERT achieves the second highest accuracy of 0.95 for this dataset. Additionally, we discuss the impact of dataset size, linguistic complexity, and model architecture on classification performance. The findings of this research contribute to ongoing efforts to improve NLP applications for low-resource languages and demonstrate the potential of LLMs in enhancing sentiment analysis tasks in Bengali. This study provides a foundation for future work in developing more efficient and domain-specific models tailored for Bengali text classification.
# I. INTRODUCTION

In recent years, Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP), marking a significant shift in how machines understand and generate human language. These models, including BERT, RoBERTa, GPT, BanglaBERT, and Llama, have demonstrated outstanding performance across a wide variety of NLP tasks, such as text classification, sentiment analysis, machine translation, question answering, and named entity recognition. What sets LLMs apart from traditional models is their ability to handle vast amounts of unstructured data, process context at scale, and generate human-like text that is often indistinguishable from that produced by humans. Leveraging deep learning techniques such as transformers, these models solve complex parameterized problems by learning from large datasets, making them an indispensable tool in the development of AI-driven language technologies. Their versatility comes from unique architectural features and a diverse set of parameters, which allow them to adapt to specific tasks and domains.

Despite the remarkable success of LLMs in widely spoken languages such as English, the application of these models to low-resource languages remains a significant challenge. Bengali, a major language spoken by millions, faces several barriers in the realm of NLP, particularly in tasks such as sentiment analysis and text classification. One of the primary reasons for these difficulties is the limited availability of high-quality, annotated datasets. In contrast to English, where vast amounts of labeled data are accessible, Bengali NLP models are hindered by a dearth of such resources. This lack of annotated data impedes the training and fine-tuning of models, making it difficult to achieve high accuracy in downstream tasks. Furthermore, while pre-trained models such as BERT and its variants have shown promise in other languages, adapting these models to work effectively with Bengali requires significant effort, including domain-specific fine-tuning and data augmentation techniques.

BanglaBERT, a pre-trained model specifically designed for the Bengali language, has emerged as a promising solution to this challenge. By leveraging transfer learning, BanglaBERT can fine-tune its parameters based on smaller, domain-specific datasets, making it well-suited for applications in Bengali NLP. However, despite the availability of models like BanglaBERT, the challenges of dealing with limited resources and optimizing performance for Bengali-specific tasks remain an ongoing area of research. As such, there is a growing interest in evaluating the performance of various LLMs on Bengali text classification tasks, particularly in niche domains such as sentiment analysis of Bengali Natok (TV drama) reviews.

This study addresses these challenges by focusing on sentiment analysis within the domain of Bengali Natok reviews, a

popular form of entertainment in Bengali-speaking regions. The Bengali Review Dataset, sourced from Kaggle, serves as the core dataset for this research. This dataset contains reviews related to Bengali Natoks, a cultural staple in Bengali-speaking communities, and presents an opportunity to analyze sentiment in a context that reflects the unique characteristics of the language and culture. This research aims to evaluate the effectiveness of five LLMs, including BanglaBERT, BERT, and other well-established models, in classifying the sentiment of these reviews. Alongside LLMs, this study also compares the performance of ten traditional machine learning models, offering a comprehensive view of how classical approaches stack up against state-of-the-art models for Bengali sentiment analysis.

To improve the performance of these models, we apply several advanced techniques, including model fine-tuning, hyper-parameter optimization, and the testing of various optimizers. These strategies are critical for overcoming the challenges posed by the dataset's inherent complexities, such as the limited size and linguistic features of Bengali. By carefully fine-tuning the models, we aim to extract the maximum potential from the available data, ensuring that the models can accurately capture the nuances of sentiment in Bengali Natok reviews.

This research makes several important contributions to both the academic and professional communities. First, it presents a thorough evaluation of sentiment analysis techniques specifically tailored for Bengali Natok reviews, contributing valuable insights to the growing body of work in Bengali NLP. Second, it offers a comparative analysis of traditional machine learning models and modern LLMs, shedding light on the relative strengths and weaknesses of each approach in the context of Bengali text classification. Finally, this study provides a deeper understanding of how LLMs can be adapted and optimized for low-resource languages like Bengali, offering guidelines for future research in the field of low-resource language processing.

The contributions of this research are summarized as follows:

* **Evaluation of Sentiment Analysis for Bengali Natok Reviews:** This study introduces a novel evaluation of sentiment analysis techniques applied to Bengali Natok reviews, contributing to the expanding field of Bengali NLP.

* **Comparison of Traditional ML Models and LLMs:** The research compares the performance of traditional machine learning models with cutting-edge LLMs, providing valuable insights into their relative effectiveness for Bengali sentiment analysis.

* **Advancing Low-Resource Language Processing:** The findings of this study will help advance the understanding of how to apply and optimize LLMs for low-resource languages like Bengali, potentially improving sentiment analysis and other NLP tasks in similar languages.
## II. RELATED WORK

Many works are done with sentiment analysis with low-level language like Bangla language. It has completed by traditional machine learning models and deep learning models.

In Yazhou Zhang et al. 2024 work, a text classification implemented with LLMs. LLaMa 2, GPT-4 and RoBERTa models are used here to train dataset. [1]

Usafa Akther Rifa et al. 2024 collected dataset with 14k samples from YouTube comments on Bangla Movies and Dramas for their research. On the dataset, they ran eight transformer models. Bangla-BERT gives 93.3% accuracy for sentiment analysis which was the best result in their analysis. [2]

Eftekhar Hossain et al. worked on the Bengali sentiment analysis. They use Multinomial Naïve Bayes for detecting sentiment polarity in Bengali book reviews. They compared several traditional machine learning techniques, including Stochastic Gradient Descent (SGD), Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. They get 84% accuracy on the validation set. Multinomial Naive Bayes performs better than the other methods. [3]

V.L. Durga et al. They applied traditional Machine Learning techniques for Bengali sentiment analysis, including Multinomial-Naive-Bayes, Logistic-Regression, Random Forest Classifier etc. are usually good techniques for text classification. Commented data from YouTube are selected as the data set. [4]

Ahmadul Karim Chowdhury et al. conducted a comprehensive study on "Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text". This paper focuses on mental health monitoring. [5]

Shahnaz Ipa et al. (2024) examined the linguistic context of product reviews written in Bangladesh. This study advances knowledge of digital customer feedback. It affects marketing and business strategy. [6]

Chong Zhang et al. (2017) examined A Case Study of Machine Translation in Financial Sentiment Analysis. Three different machine translation engines, including Microsoft, Google, and Google Neural Network, were used to translate this gold standard corpus into the target language (German). Firstly, this paper compares Sentiment between Gold standard and Human. Then it compares with Gold standard with Machines. After that, it compares Human vs Machines. All of this is observed by its mean values. [7]

Mohsinul Kabir et al. (2024) worked on a paper which demonstrated impressive zero-shot capabilities in various languages. This paper shows the evaluation performance of LLMs—GPT-3.5, LLaMA-2-13b-chat, and Claude-2 in the Bangla language. It works for text classification, sentiment analysis, and question answering. [8]

Hemal and Hasan (2024) had done a sentiment analysis using BanglaBERT as a pre-trained model for Bangla text. 15,000 data collected manually and constructed a lexicon data dictionary that assigns polarity scores. [9]

Anirban and Abhik (2021) investigated the transformer model which is suitable for Bangla sentiment analysis and provides better accuracy. Specially, this paper experiments with multilingual BERT model and RoBERTa. This paper shows about the effectiveness of transformer-based model in Bangla sentiment analysis. It achieves 95% accuracy. [10]

## III. DATASETS

### A. Dataset Description

Bangla Review Dataset is a bengali natok review dataset based on sentiment labeling. Bangla is low-resource language, that's why it has some limitation of getting a proper dataset. This dataset has multiple labels, including Reviews and Sentiment. This dataset is suitable for doing sentiment analysis according to both ML and LLM models. This dataset is taken from an open source named Kaggle. This dataset has proper labeling. It's reviews are written in Bangla and sentiments are written in English with Positive and Negative declaration. This dataset is created as it can be used for the training and testing of ML models. This dataset is perfectly applicable for sentiment analysis and natok (social media) analytics. [11]

### B. Dataset Features

In sentiment analysis, it requires only two features from the dataset, including Reviews and Sentiment.

### C. Feature Distribution

This dataset contains 11,807 data with its unique reviews (10,763).

<table><caption>TABLE I FEATURES OF THE DATASET</caption><thead><tr><td>Comment</td><td>Label</td></tr></thead><tbody><tr><td>এক কথায় অসাধারণ।</td><td>Positive</td></tr><tr><td>ভালো লাগলো।</td><td>Positive</td></tr><tr><td>কি বল বানাইছে।</td><td>Negative</td></tr><tr><td>ফালতু নাটক।</td><td>Negative</td></tr><tr><td>নাটক টা দাক্ষণ লাগচে।</td><td>Positive</td></tr><tr><td>সেই রকম তুয়া।</td><td>Negative</td></tr></tbody></table>

### D. Data Limitations

The Bengali data is already a low resource language. The availability of proper data is short. The dataset were used in this study was shorter in size and this dataset contains 8500 negative labeled data and 3307 positive labeled data. Which makes the data positive biased.

## IV. METHODOLOGY

### A. Data Pre-processing

This dataset needs to do pre-processing to get a clean dataset so that every model can run smoothly. This is in CSV(Comma Separated Value) form. There is some process to do data pre-processing. At first, Pandas.library in python is needed for loading this dataset. By calling shape() function, it shows data size. Isnull() function shows any null values is present
or not. If there has any null values then by invoking dropna() function all null values will be removed. The null values were dealt with dropping method to completely avoid the gap. There is 8500 Positive data and 3307 Negative data. It is declared 0 for Negative sentiment and 1 for Positive Sentiment.

## B. Proposed Methodology

Proper workings of applicable models, there is some required libraries, need to import. After completing data pre-processing new dataset is ready to train. After that, models performance will be measured by evaluation matrix.

<figure><img src="image_3.png" /><figcaption>Fig. 1. The Proposed Methodology for Fine-Tuning LLMs for Bengali Sentiment Analysis</figcaption></figure>

## C. Large Language Models (BanglaBERT)

BanglaBERT model gives highest result according to this dataset. It is a Bert based model. BanglaBERT is specially created for bangla language. This dataset is trained with multiple data. It has 32K tokens as vocabulary size. This model is famous for bangla datasets and multilingual tasks also.

* Transfer Learning:

Achieving optimal performance with BanglaBERT in a niche domain requires a large, domain-specific dataset for training.

* Parameter Optimization:
  For getting a better accuracy, a specific parts need to do fine-tuning.

* Training Configuration:

- Optimizer:

- Learning rate: $2 \times 10^{-5}$ with a gradual decay scheduler.

- Batch Size: 16, for balancing between memory requirements and speed of convergence.

- Epochs: 10 for each optimizers for fine-tuning stable model.

## D. Large Language Models (BERT)

BERT(bert-base-uncased) is also used on this dataset, which achieved 95% accuracy after fine-tuning which was the second best. Learning rate was $2 \times 10^{-5}$, Batch size was 16 and number of epochs were 10.

## E. Traditional Machine Learning Models

10 traditional models of ML also taken here and make a comparative analysis of their performance and training. Applicable models are mentioned below:

* Logistic Regression

* Random Forest

* Support-Vector-Machine(SVM)

* Decision Tree

* XGBoost

* Multinomial Naïve Bayes

* K-Nearest Neighbors(KNN)

* Gradient Boosting

* LightGBM

* AdaBoost

## F. Evaluation Metrics

The performance of BanglaBERT, BERT and comparable traditional models assessed through a comprehensive set of metricse:

* Accuracy: As a measure of the model's overall performance, accuracy is calculated by dividing the number of accurate predictions by the total number of forecasts.

$$ Accuracy = \frac{TP + TN}{TP + TN + FP + FN} \quad (1) $$

* Precision: Precision is for positive predictions. It represents the number of relevant instances among the retrieved instances.

$$ Precision = \frac{TP}{TP + FP} \quad (2) $$

* Recall: Recall describes how well a model can find all relevant cases in a dataset. This is the percentage of relevant instances of the total relevant instances that were retrieved by the specific model.

$$ Recall = \frac{TP}{TP + FN} \quad (3) $$
* F1-Score: Precision (harmonic mean) and recall are combined into a single rating. It has done by the F1 score. It allows to aggregate both metrics and gives one value that represents both properties.

$$ \text{F1-Score} = \frac{2 \times \text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} \quad (4) $$

* Confusion Matrix: Tabular representation showing the True Positives, True Negatives, False Positives and False Negatives.

## G. Hyper-parameter Tuning

Hyper-parameter tuning is required for fast training purpose and better accuracy. In this paper, hyper-parameters are adjusted like learning rate, num of epochs, batch size, and weight decays.

## V. RESULT EVALUATION

In this TABLE II, represents comparison of the accuracy scores of the traditional machine learning models and LLMs (BanglaBERT). BanglaBERT achieves the highest accuracy of 0.97. However, the LLM (BERT) has the second highest accuracy of 0.95.

<table><caption>TABLE II MODEL ACCURACY</caption><thead><tr><td>Model</td><td>Accuracy</td></tr></thead><tbody><tr><td>BanglaBERT</td><td>0.97</td></tr><tr><td>BERT</td><td>0.95</td></tr><tr><td>Logistic Regression</td><td>0.87</td></tr><tr><td>Random Forest</td><td>0.87</td></tr><tr><td>SVM</td><td>0.85</td></tr><tr><td>Decision Tree</td><td>0.83</td></tr><tr><td>Naive Bayes</td><td>0.87</td></tr><tr><td>KNN</td><td>0.84</td></tr><tr><td>Gradient Boosting</td><td>0.83</td></tr><tr><td>AdaBoost</td><td>0.79</td></tr><tr><td>XGBoost</td><td>0.85</td></tr><tr><td>LightGBM</td><td>0.86</td></tr></tbody></table>

<caption>TABLE III PERFORMANCE METRICS OF POSITIVE LABEL</caption>

<caption>TABLE IV PERFORMANCE METRICS OF NEGATIVE LABEL</caption>

In this TABLE III, and IV shows how the three metrics, Precision, Recall, and F1 Score are performing on two different labels, Positive and Negative for various machine learning models and the LLMs (BanglaBERT, BERT and Llama 3.1). Models having the highest F1 scores for Positive labels (0.98) and for Negative labels (0.95). These models also have different performance depending on the label and evaluation criteria.

In Figure 2, the performance for two labels, Positive, and Negative is shown in the confusion matrix. The values on the

<table><thead><tr><td>Model</td><td>Precision</td><td>Recall</td><td>F1 Score</td></tr></thead><tbody><tr><td>BanglaBERT</td><td>0.98</td><td>0.98</td><td>0.98</td></tr><tr><td>BERT</td><td>0.97</td><td>0.96</td><td>0.96</td></tr><tr><td>Logistic Regression</td><td>0.88</td><td>0.95</td><td>0.91</td></tr><tr><td>Random Forest</td><td>0.88</td><td>0.94</td><td>0.91</td></tr><tr><td>SVM</td><td>0.85</td><td>0.96</td><td>0.90</td></tr><tr><td>Decision Tree</td><td>0.89</td><td>0.88</td><td>0.88</td></tr><tr><td>Naive Bayes</td><td>0.87</td><td>0.95</td><td>0.91</td></tr><tr><td>KNN</td><td>0.86</td><td>0.92</td><td>0.89</td></tr><tr><td>Gradient Boosting</td><td>0.81</td><td>0.98</td><td>0.89</td></tr><tr><td>AdaBoost</td><td>0.78</td><td>0.98</td><td>0.87</td></tr><tr><td>XGBoost</td><td>0.86</td><td>0.95</td><td>0.90</td></tr><tr><td>LightGBM</td><td>0.89</td><td>0.93</td><td>0.91</td></tr></tbody></table>

<table><thead><tr><td>Model</td><td>Precision</td><td>Recall</td><td>F1 Score</td></tr></thead><tbody><tr><td>BanglaBERT</td><td>0.95</td><td>0.94</td><td>0.95</td></tr><tr><td>BERT</td><td>0.90</td><td>0.92</td><td>0.91</td></tr><tr><td>Logistic Regression</td><td>0.84</td><td>0.67</td><td>0.74</td></tr><tr><td>Random Forest</td><td>0.82</td><td>0.70</td><td>0.75</td></tr><tr><td>SVM</td><td>0.85</td><td>0.58</td><td>0.69</td></tr><tr><td>Decision Tree</td><td>0.70</td><td>0.72</td><td>0.71</td></tr><tr><td>Naive Bayes</td><td>0.85</td><td>0.66</td><td>0.74</td></tr><tr><td>KNN</td><td>0.76</td><td>0.63</td><td>0.69</td></tr><tr><td>Gradient Boosting</td><td>0.91</td><td>0.45</td><td>0.60</td></tr><tr><td>AdaBoost</td><td>0.88</td><td>0.33</td><td>0.48</td></tr><tr><td>XGBoost</td><td>0.84</td><td>0.61</td><td>0.71</td></tr><tr><td>LightGBM</td><td>0.80</td><td>0.70</td><td>0.75</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Confusion Matrix of LLMs (BanglaBERT)</figcaption></figure>

diagonal are the values that got predicted correctly, for Positive label, model is performing better than for Negative.

## VI. CONCLUSION AND FUTURE WORK

In this paper, we have presented a comprehensive comparative analysis of Large Language Models (LLMs) and traditional machine learning models for sentiment analysis of Bengali Natok reviews. Despite the challenges posed by the scarcity of annotated datasets for Bengali, our study
demonstrated the significant potential of pre-trained models, such as BanglaBERT and BERT, in enhancing the accuracy and robustness of sentiment classification tasks. Our findings reveal that LLMs outperform traditional machine learning models in terms of both accuracy and F1 score, illustrating their superiority in handling complex linguistic structures and semantic nuances inherent in the Bengali language.

Furthermore, the application of model fine-tuning and the use of various optimizers have proven to be crucial in boosting the performance of these models. This approach has provided valuable insights into the optimization of LLMs for sentiment analysis tasks in low-resource languages, especially in the context of Bengali. By leveraging transfer learning techniques and domain-specific fine-tuning, LLMs such as BanglaBERT have shown remarkable adaptability, even when trained on smaller datasets.

This research makes a significant contribution to the growing field of Bengali NLP, offering an in-depth evaluation of sentiment analysis techniques applied to a domain-specific dataset. It not only sheds light on the relative strengths and weaknesses of both LLMs and traditional machine learning models, but also provides a broader understanding of how LLMs can be optimized for low-resource languages like Bengali. Our findings serve as a foundation for future work in sentiment analysis and other NLP tasks for Bengali and other similar low-resource languages, helping to shape the development of more robust and efficient NLP systems.

**Future Work:** While this study offers valuable insights into sentiment analysis for Bengali Natok reviews, several avenues for future research and improvements remain. Addressing these challenges could further enhance model performance and expand the applicability of sentiment analysis systems in Bengali and other low-resource languages.

* Larger Datasets: Expanding datasets across domains like movies, politics, and social media will improve generalization.

* Multi-Task Learning: Combining sentiment analysis with emotion detection or topic classification could boost model performance.

* Cross-Lingual Transfer: Using models trained on larger languages (e.g., English) can help mitigate data scarcity in Bengali.

* Model Interpretability: Enhancing interpretability will help users better understand model predictions, particularly for subjective tasks.

* Real-Time Analysis: Implementing real-time sentiment analysis for dynamic data sources like social media and news platforms.

Addressing these areas will advance Bengali NLP and sentiment analysis, improving systems for Bengali and other low-resource languages.

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