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# Bengali Sentiment Analysis: Fine-Tuning LLMs and Comparing with Traditional Machine Learning Models for Bengali Movie Review Dataset

Khan Md Abdur Rahman, Fairooz Binta Kabir, Maher Ali Rusho, Samia Muntaha, Md. Rakib Hasan, Md. Naib Hossain Khan, Md Ashfaqul Azam Chowdhury, Md Khurshid Jahan, Mithila Arman

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Khan Md Abdur Rahman, Fairooz Binta Kabir, Maher Ali Rusho, Samia Muntaha, Md. Rakib Hasan, et al.. Bengali Sentiment Analysis: Fine-Tuning LLMs and Comparing with Traditional Machine Learning Models for Bengali Movie Review Dataset. 2025. hal-05068208v1

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Submitted on 15 May 2025 (v1), last revised 4 Jun 2025 (v2)

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# Bengali Sentiment Analysis : Fine-Tuning LLMs and Comparing with Traditional Machine Learning Models for Bengali Movie Review Dataset

1st Khan Md Abdur Rahman

Dept. of Applied Science (Engineering)
UCSI University
56000-Wilayah Persekutuan, Kuala Lumpur, Malaysia
Khanxmain@gmail.com

2nd Fairooz Binta Kabir

Viqarunnisa Noon College
Dhaka, Bangladesh
fairooz2207@gmail.com

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

4th Samia Muntaha

Dept. of CSE
Chittagong Independent University
Chittagong, Bangladesh
samiamuntaha@gmail.com

5th Md. Rakib Hasan

Dept. of Information & Communication Technology
Comilla University
Comilla, Bangladesh
rakib@cou.ac.bd

6th Md. Naib Hossain Khan

Dept. of CSE
Daffodil International University
Dhaka, Bangladesh
251-25-039@diu.edu.bd

7th Md Ashfaqul Azam Chowdhury

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

8th Md. Khurshid Jahan

Dept. of ECE
North South University
Dhaka, Bangladesh
khurshid.jahan@northsouth.edu

9th Mithila Arman

Dept. of CSE
BRAC University
Dhaka, Bangladesh
mithila.arman@g.bracu.ac.bd

**Abstract**—In this study, sentiment analysis of bengali movie review text data based on large language models (LLMs) along with the traditional machine learning approaches. There were some challenges due to linguistic complexity and limited annotated datasets in bengali text. The research leads to satisfy the lack of comparative studies on low-resource languages like Bengali in a systematic way. Bangla Movie and Drama Comments Sentiment Dataset was taken for applying five LLMs so that The usefulness of large language models (LLMs) is shown here and traditional machine learning (ML) also. In this study, fine-tuning five LLMs, including BERT, and RoBERTa which have advanced techniques such as LoRA and mixed-precision training. Additionally, this is compared with ten traditional ML models, including Logistic Regression, Random Forest, and Support Vector Machines. RoBERTa achieves the highest accuracy (88%) and Bert achieves the second highest accuracy (83%). This research provides optimization of bengali movie review sentiment analysis. Depending on the goals, there are always future directions to pursue, such as further development of bangali movie review of datasets.

**Index Terms**—Sentiment Analysis, Large Language Models (LLMs), Machine Learning (ML), Text Classification, Fine-

Tuning, Hyper Parameter Tuning, Natural Language Processing (NLP), Low-Resource Languages (LoRA), RoBERTa, BERT.

## I. INTRODUCTION

Recently, Large Language Models (LLMs) have taken over the area of Natural Language Processing (NLP), significantly improving the performance of text classification tasks across various languages. BERT-based models, such as BERT, RoBERTa, and DeBERTa, have shown remarkable success in understanding complex language patterns, enabling them to outperform traditional Machine Learning (ML) models in most of the NLP applications, including sentiment analysis, named entity recognition, and question answering. The rapid advancements in LLMs, driven by larger model architectures, more diverse pretraining datasets, and sophisticated fine-tuning techniques. [1] [2]

Despite these advancements, challenges remain for languages like Bengali, where limited annotated datasets and computational resources hinder the development of effective
sentiment analysis tools. Bengali sentiment analysis is par-
ticularly under explored, and existing research predominantly
relies on ML models, which often struggle with the linguistic
complexities and nuances of the language. With the increasing
volume of Bengali digital content, particularly in areas like
social media, customer feedback, and movie reviews, there is
a pressing need for advanced sentiment analysis tools that can
accurately capture the subtleties of the Bengali language. [3]

This comparative analysis was designed to provide insight-
ful information on the strengths and weaknesses of both
approaches, guiding researchers and practitioners in selecting
the most suitable model for Bengali sentiment analysis. The
ultimate goal is to achieve the highest possible accuracy for
sentiment classification of Bengali movie reviews, addressing
the challenges of linguistic complexity, data scarcity, and
model limitations.

While the research findings are specific to Bengali sentiment
analysis, they contribute to the broader field of NLP, offering
valuable insights into model fine-tuning and optimization in
low-resource languages. This work shows the comparative
analysis of 5 LLMs (including RoBERTa and BERT) and 10
traditional machine learning algorithms. The implications of
these findings extend to both academic and industrial settings,
where sentiment analysis and opinion mining play a critical
role in various domains, including social media monitoring,
customer feedback analysis, and more.

The main contributions of this work are as follows:

* This study presents the development and evaluation of a robust sentiment analysis framework designed specifically for Bengali movie reviews, combining the strengths of large language models and traditional machine learning models.

* Fine-tuning RoBERTa and BERT on the dataset using LoRA, mixed-precision training, and systematic hyper-parameter tuning, achieving the highest accuracy compared to traditional models.

* Establishing strong baselines with traditional machine learning models to provide a clear performance comparison with LLMs.

* Highlighting the challenges of linguistic complexity and data scarcity in Bengali sentiment analysis, with suggestions for improving model performance through better data augmentation and bangali movie and drama comments sentiment techniques.

## II. RELATED WORK

Several studies have explored in the Bengali language,
leveraging both traditional machine learning methods and
modern deep learning approaches.

In Abdul et al. 2021 work, a sentiment analysis dataset
named Bangla Movie and Drama Comments Sentiment
Dataset was introduced, created with Bengali movie and
drama comments collected from various sources like YouTube
comments. Unlike most sentiment analysis datasets, this one
includes informal, conversational text that features a wide
range of linguistic variations and domain-specific terminology.

Each comment was annotated with one of three sentiment
labels: positive, negative, and neutral. [4]

Shahnaz Ipa et al. 2024 conducted research that investigate the
linguistic landscape of Bangladeshi product reviews. [5]

Usafa Akther Rifa et al. 2024 conducted research where they
introduced their manually collected dataset with 14k samples
from YouTube comments on Bangla Movies and Dramas. They
performed 8 transformers models on that dataset. They have
mentioned they got around 93.3% accuracy using Bangla-
BERT for sentiment analysis which was the best result in their
analysis. [6]

Hemal Mahmud et al. 2024 conducted research on sentiment
analysis in Bengali texts. The authors used pretrained large-
language-models like Bangla-BERT for enhancing the senti-
ment analysis for Bengali language. The authors also used
Bangla Sentiment Polarity Score (BSPS) a novel rule based
algorithm with the Bangla-BERT model as a hybrid approach
for enhanced sentiment analysis in Bengali and suggest path-
ways for future research and application in languages with
similar linguistic complexities. [7] [8]

Eftekhar Hossain et al. they also added their contribution on
the Bengali sentiment analysis. They research on Sentiment
Polarity Detection on Bengali Book Reviews Using Multi-
nomial Naïve Bayes. They conducted a comparative analysis
with various Traditional Machine Learning approaches such
as Logistic Regression, Naive Bayes, Support Vector Ma-
chine(SVM), and Stochastic Gradient Descent(SGD). Their
experimental result reveals that the multinomial naive Bayes
outperforms the other techniques with 84% accuracy on the
validation set. [9]

Kai Yin et al. 2024 they introduced CrisisSense-LLM which is the fine-tuned version of Large Language Models (LLMs) for Multi-label Social Media Text Classification in Disaster Informatics. They performed Prompt Engineering, Retrieval Augmented Generation(RAG), and FineTuning using LoRA and Full-Tuning. They used Full_Tune models for classification of the Disaster Information and gained 96.7% accuracy. [10]

## III. DATASETS

### A. Dataset Description

Bangla Movie and Drama Comments Sentiment Dataset, which is created for sentiment analysis on Bangla Language, developing systems to analyze audience preference for entertainment content, and identifying trends in user sentiment towards movies and dramas. Though, it is noted that low-resource language like Bengali does not have that type of any proper dataset that is both large and standard. As a result, recent research works with Bangla language in sentiment analysis have fallen short to provide better results. In this dataset, a substantial textual dataset of Bangla texts have been provided which was processed, multiple validated, multi-labeled, and ready for sentiment analysis implementation and text-classification experiments. The data is collected from open-source Kaggle. This collection of Bangla texts, each marked with a positive, negative, or neutral sentiment, is
designed for machine learning applications. the dataset was split into 70% training, 10% validation, and 20% testing sets. Before modeling, basic preprocessing steps such as lowercasing, punctuation removal, and whitespace normalization were applied. This dataset serves as a benchmark for evaluating both classical and modern deep learning models in Bengali sentiment analysis. It provides labeled Bangla texts, categorized by sentiment, for training and testing machine learning models capable of handling non-standard language. This resource is particularly useful for sentiment analysis and social media analytics. [4]

## B. Dataset Features

There are Multiple features in the dataset, but while sentiment analysis two of the features were counted and measured for the experiment.

<table><caption>TABLE-I Features of the Dataset</caption><thead><tr><td>Comment</td><td>Label</td></tr></thead><tbody><tr><td>গল্পটা ঠিকঠাক, তবে কিছু জায়গায় লব্ধা মনে হয়েছে।</td><td>Neutral</td></tr><tr><td>অসাধারণ অসাধারণ।</td><td>Positive</td></tr><tr><td>বাংলাদেশের বেস্ট একটা ছবি।</td><td>Positive</td></tr><tr><td>ব্যবসাসফল না ডিরেক্টর নিজেই বলেছেন।</td><td>Negative</td></tr><tr><td>পছন্দের মুভি।</td><td>Positive</td></tr><tr><td>বেশি ভালো লাগে নাই মুভি টা।।</td><td>Negative</td></tr></tbody></table>

## C. Feature Distribution

It consists of 9000 rows of the dataset with all unique values. The distribution of the unique feature labels are shown as a pie-plot.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Pie-Plot of the Feature Distribution of the Dataset.</figcaption></figure>

# IV. METHODOLOGY

## A. Data Pre-processing

The dataset was pre-processed in several stages to ensure clean and consistent data for model training. The data was in

a structured CSV(Comma Separated Values) file. Firstly, the data was loaded using `read_csv()` function from `pandas` library. Then, text cleaning was performed using `dropna()`, which involved removing stopwords, punctuation, and any missing values to enhance the quality of the data. Tokenization was carried out using the `Autorecommend` from the `transformers` (from Hugging-Face) library, ensuring that the text was broken down into tokens in a consistent manner. Additionally, all text was converted to lowercase during tokenization to prevent any discrepancies due to case sensitivity. Also, applied `BackTranslation` for better outcome for multilingual tasks. The data augmentation techniques were also used `Enhanced Data Augmentation` (EDA). To handle class imbalance the `RandomOver Sampler` (ROS) was used.

The labels in the dataset were initially in float format (0.0 for Negative, 1.0 for Positive, 2.0 for Neutral).

## B. Proposed Methodology

The proposed methodology starts with setting up the environment by importing and loading required libraries. Then preparing the data using file handling and preprocessing techniques. Then it gets ready to be trained. After training the performance of the model needs to be measured using evaluation metrics. The Figure 2 is the work-flow-diagram of the Proposed Methodology.

## C. Large Language Models (RoBERTa)

RoBERTa(xlm-roberta-base) is the main fine-tuned model with better results in this dataset. RoBERTa is a huge model with 100 language support. It is trained with multilingual corpora training data. It has around 250k tokens as vocabulary size. This is mainly used for multilingual/cross-lingual tasks. Some key points of the fine-tuning process is given below :

* **Transfer Learning:** Achieving optimal performance with RoBERTa in a niche domain necessitates a large, specialized dataset for training.

* **Parameter Optimization:** To efficiently fine-tune specific parts of the model while maintaining accuracy, techniques like LoRA(Low-Rank Adaptation) are used, offering a good compromise between computational resources and model performance.

* **Training Configuration:**
  - Optimizer: AdamW for adaptive Learning, Lion for reduce memory usage, SGD for fundamental optimization.

- Learning rate: $3 \times 10^{-5}$ with a gradual decay scheduler.

- Batch Size: 16 for a good balance between memory requirements and speed of convergence.

- Epochs: 30 for each optimizers for fine-tuning stable model.
<figure><img src="image_4.png" /><figcaption>Fig. 2. The Proposed Methodology for Fine-Tuning LLMs for Bengali Sentiment Analysis</figcaption></figure>

## D. Large Language Models (BERT)

BERT(bert-base-uncased) is the second highest fine-tuned model that achieved strong results on this dataset. Every aspects of the BERT model is as same as RoBERTa model. But, the training configuration of BERT are a the main differentiator. For BERT AdaFactor was used as optimizer instead of Lion, the learning rate was 2 × 10⁻⁴, the batch size was kept same as RoBERTa (Batch Size = 16), and the number of epochs were total 30, where the first 1-14 epochs were for AdamW, the epochs no. of 15-29 were for AdaFactor and the rest epochs were for SGD optimizer.

## E. Traditional Machine Learning Models

A comparative analysis was performed, training and validating 10 traditional machine learning algorithms. The following 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

* CatBoost

## F. Evaluation Metrics

The performance of RoBERTa, BERT and comparable traditional models assessed through a comprehensive set of metrics:

* **Accuracy:** Accuracy is the representation of correct predictions divided by the total number of predictions, representative of the performance of the model as a whole.

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

* **Precision:** Precision for the 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:** The h-mean(harmonic mean) of precision and recall is called F1-Score. It allows to aggregate both metrics and give one value that represents both properties.

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

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

## G. Hyperparameter Tuning

While finetuning RoBERTa and BERT model adjusted hyperparameters like learning rate, num of epochs, batch size, and weight decays. Also performed GridSearch HyperParameter Tuning to get the best parameters. Likewise, traditional machine learning models hyperparameters like the number of estimators, maximum depth of trees, and regularization terms tuned. These modifications serve to control the term complexity of the models while preventing overfitting as well ensuring that they perform well on different datasets through successful bias-variance trade off.
## H. Optimization Techniques

Though LLMs performs better than traditional Machine Learning models, getting a better result is quite tough. The hyperparameter tuning was not enough for tuning the models for these types of datasets. AdamW, Lion and SGD were used as the optimization technique to optimize the process and get better result.

## V. RESULT EVALUATION

In this TABLE II, represents comparison of the accuracy scores of the traditional machine learning models and LLMs (RoBERTa, and BERT). RoBERTa performs enormously better in terms of accuracy with the score of 0.88 while BERT achieves the accuracy score of 0.83. It is also visible that the best performed traditional machine learning model is Support-Vector-Machine (SVM) with the accuracy of 0.70 and also the Cat-Boost-Classifier performed better with the same accuracy score of 0.70 which are the highest for traditional machine learning methods.

<table><caption>TABLE-II Model Accuracy</caption><thead><tr><td>Model</td><td>Accuracy</td></tr></thead><tbody><tr><td>RoBERTa</td><td>0.88</td></tr><tr><td>BERT</td><td>0.83</td></tr><tr><td>Logistic Regression</td><td>0.69</td></tr><tr><td>Random Forest Classifier</td><td>0.66</td></tr><tr><td>Support Vector Machine (SVM)</td><td>0.7</td></tr><tr><td>Decision Tree Classifier</td><td>0.56</td></tr><tr><td>XGBClassifier</td><td>0.69</td></tr><tr><td>Multinomial Naive Bayes</td><td>0.67</td></tr><tr><td>KNeighbors Classifier</td><td>0.53</td></tr><tr><td>Gradient Boosting Classifier</td><td>0.67</td></tr><tr><td>LightGBM Classifier</td><td>0.68</td></tr><tr><td>CatBoost Classifier</td><td>0.7</td></tr></tbody></table>

This Table-II represents the total accuracy of the LLM models and the Machine-Learning models. Both RoBERTa and BERT models accuracy on the dataset and the accuracy of the Machine-Learning models are mentioned here.

<table><caption>TABLE-III Performance Metrics of Negative Label</caption><thead><tr><td>Model</td><td>Precision</td><td>Recall</td><td>F1 Score</td></tr></thead><tbody><tr><td>RoBERTa</td><td>0.91</td><td>0.83</td><td>0.87</td></tr><tr><td>BERT</td><td>0.78</td><td>0.82</td><td>0.8</td></tr><tr><td>Logistic Regression</td><td>0.64</td><td>0.63</td><td>0.64</td></tr><tr><td>Random Forest Classifier</td><td>0.62</td><td>0.58</td><td>0.6</td></tr><tr><td>Support Vector Machine (SVM)</td><td>0.63</td><td>0.67</td><td>0.65</td></tr><tr><td>Decision Tree Classifier</td><td>0.52</td><td>0.55</td><td>0.54</td></tr><tr><td>XGBClassifier</td><td>0.66</td><td>0.65</td><td>0.65</td></tr><tr><td>Multinomial Naive Bayes</td><td>0.63</td><td>0.55</td><td>0.59</td></tr><tr><td>KNeighbors Classifier</td><td>0.48</td><td>0.5</td><td>0.49</td></tr><tr><td>Gradient Boosting Classifier</td><td>0.58</td><td>0.73</td><td>0.65</td></tr><tr><td>LightGBM Classifier</td><td>0.65</td><td>0.63</td><td>0.64</td></tr><tr><td>CatBoost Classifier</td><td>0.65</td><td>0.67</td><td>0.66</td></tr></tbody></table>

<table><caption>TABLE-IV Performance Metrics of Neutral Label</caption><thead><tr><td>Model</td><td>Precision</td><td>Recall</td><td>F1 Score</td></tr></thead><tbody><tr><td>RoBERTa</td><td>0.81</td><td>0.89</td><td>0.85</td></tr><tr><td>BERT</td><td>0.84</td><td>0.79</td><td>0.81</td></tr><tr><td>Logistic Regression</td><td>0.7</td><td>0.71</td><td>0.71</td></tr><tr><td>Random Forest Classifier</td><td>0.66</td><td>0.68</td><td>0.67</td></tr><tr><td>Support Vector Machine (SVM)</td><td>0.77</td><td>0.68</td><td>0.72</td></tr><tr><td>Decision Tree Classifier</td><td>0.57</td><td>0.59</td><td>0.58</td></tr><tr><td>XGBClassifier</td><td>0.7</td><td>0.74</td><td>0.72</td></tr><tr><td>Multinomial Naive Bayes</td><td>0.67</td><td>0.74</td><td>0.7</td></tr><tr><td>KNeighbors Classifier</td><td>0.51</td><td>0.69</td><td>0.58</td></tr><tr><td>Gradient Boosting Classifier</td><td>0.79</td><td>0.59</td><td>0.67</td></tr><tr><td>LightGBM Classifier</td><td>0.69</td><td>0.68</td><td>0.69</td></tr><tr><td>CatBoost Classifier</td><td>0.75</td><td>0.68</td><td>0.71</td></tr></tbody></table>

<table><caption>TABLE-V Performance Metrics of Positive Label</caption><thead><tr><td>Model</td><td>Precision</td><td>Recall</td><td>F1 Score</td></tr></thead><tbody><tr><td>RoBERTa</td><td>0.92</td><td>0.92</td><td>0.92</td></tr><tr><td>BERT</td><td>0.87</td><td>0.86</td><td>0.87</td></tr><tr><td>Logistic Regression</td><td>0.72</td><td>0.73</td><td>0.72</td></tr><tr><td>Random Forest Classifier</td><td>0.7</td><td>0.72</td><td>0.71</td></tr><tr><td>Support Vector Machine (SVM)</td><td>0.71</td><td>0.73</td><td>0.72</td></tr><tr><td>Decision Tree Classifier</td><td>0.61</td><td>0.56</td><td>0.59</td></tr><tr><td>XGBClassifier</td><td>0.72</td><td>0.68</td><td>0.7</td></tr><tr><td>Multinomial Naive Bayes</td><td>0.7</td><td>0.72</td><td>0.71</td></tr><tr><td>KNeighbors Classifier</td><td>0.69</td><td>0.4</td><td>0.51</td></tr><tr><td>Gradient Boosting Classifier</td><td>0.7</td><td>0.69</td><td>0.69</td></tr><tr><td>LightGBM Classifier</td><td>0.7</td><td>0.73</td><td>0.72</td></tr><tr><td>CatBoost Classifier</td><td>0.71</td><td>0.75</td><td>0.73</td></tr></tbody></table>

These Table-III represents the performance metrics of the Negative Label of the dataset. Whereas, the Table-IV and Table-V represents the performance metrics of Neutral and Positive Labels. These performance metrics were measured from Precision, Recall, F1-Score.

From the mentioned tables and the confusion matrices, it is visible that the RoBERTa model is performing best. It is also mentionable that, BERT is also performing better than the traditional machine learning models. By keeping in consideration of the precision, recall, and f1-score values for all the three categories, the RoBERTa and BERT model is making a huge advancement.

## VI. CONCLUSION AND FUTURE WORK

The study investigated sentiment analysis on Bangla Movie and Drama Review Dataset using both advanced language models like RoBERTa, BERT, and traditional machine learning techniques. Fine-tuning RoBERTa with parameter-efficient
<figure><img src="image_5.png" /><figcaption>Fig. 3. Confusion Matrix of Fine-Tuned RoBERTa Model</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Confusion Matrix of Fine-Tuned BERT Model</figcaption></figure>

methods such as LoRA achieved the highest accuracy of 88% in sentiment classification. On the other hand, traditional models like Support Vector Machine and Logistic Regression provided valuable interpret-ability regarding the dataset's nuances, attaining accuracies of 70% and 69%, respectively. These results highlight the challenges of working with low-resource data while demonstrating the capability of large language models to handle linguistic ambiguities effectively. The comparative results clearly indicate that large language models (LLMs) outperform traditional machine learning models in all evaluation metrics. This performance gain can be attributed to the pre-training of LLMs on multilingual corpora, which makes them better suited for understanding the morphological and syntactic complexities of Bengali. While traditional

models are lightweight and easier to train, they fail to capture contextual nuances and suffer in handling code-mixed or informal text, which is common in user-generated content. On the other hand, LLMs, though computationally expensive, achieve better generalization and semantic understanding. This study highlights the trade-off between accuracy and computational resource requirement.

Future research will focus on enhancing the sentiment analysis techniques for Bangla texts, particularly in handling mixed scripts, dialectical variations, and unconventional linguistic expressions. Expanding the dataset to include diverse domains and incorporating more fine-grained sentiment annotations could further improve model generalization and can focus on optimizing smaller models for low-resource environments or applying knowledge distillation techniques.

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