This is a large-scale Bangla dataset based on positive, negative, and and neutral comments. It has four features: platform, where we get the comments; sources; comment; sentiment; and label.

There are four columns which are Platform, Comment, Sentiment, and Label. I have collected Bangla comments from Twitter, Youtube, and Google. Comment is about positive, negative, and neutral. Sentiment is about making toxic, neutral, sad, funny, and happy comments that are labeled by 0, 1, 2, 3, and 4.

This is a dataset for Bengali sentiment analysis which a merged dataset from the publicly available sentiment dataset. The sources I used to make a merged bangla sentiment dataset are: 1) https://www.kaggle.com/datasets/cryptexcode/sentnob-sentiment-analysis-in-noisy-bangla-texts 2) https://github.com/atik-05/Bangla_ABSA_Datasets/tree/master 3) https://data.mendeley.com/datasets/n53xt69gnf/3 4) https://github.com/shakkhor/Academic-Thesis/blob/master/450/comments.csv 5) https://github.com/mohsinulkabir14/BanglaBook/tree/main/data/csv After that, i applied some cleaning and preprocessing on this merge dataset. In the dataset, there are 2 columns. One is "Data" and another is "Label". There are 3 labels for sentiment labeling. 1) Neutral : 0 2) Positive : 1 3) Negative: 2

Bengali Sentiment Analysis Dataset

  Dataset Description

This dataset contains 44,236 Bengali sentences with corresponding sentiment labels, synthetically generated using ChatGPT for natural language processing and machine learning research.

  Dataset Summary

Language: Bengali (বাংলা) Total Entries: 44,236 synthetic sentences Task: Sentiment Classification Format: JSON Generation Method: OpenAI ChatGPT (GPT-4) License: CC0 1.0 Universal (Public Domain)… See the full description on the dataset page: https://huggingface.co/datasets/shaikh25/synthetic-bengali-sentiment.

The Bangla Product Comments Dataset is a comprehensive collection of product reviews gathered from diverse ecommerce platforms in Bangladesh. This dataset offers a rich source of information reflecting customer opinions and sentiments towards various products available online. This dataset holds significant value for businesses, researchers, and data scientists interested in understanding consumer behavior, product perception, and sentiment analysis within the Bangladeshi ecommerce landscape. By leveraging this dataset, stakeholders can derive actionable insights to enhance product quality, marketing strategies, and overall customer satisfaction.

Columns:

1. Product_ID: A unique identifier for each product, facilitating organization and referencing.
2. Date: The date when the comment was posted, providing temporal context for analysis.
3. Customer Name: The name or identifier of the customer who submitted the comment, ensuring traceability and potential user segmentation.
4. Rating: A numerical representation (typically on a scale of 1 to 5) reflecting the customer's overall satisfaction level with the product.
5. Label Sentiment: A categorical label assigned to each comment indicating the sentiment expressed by the customer (e.g., positive, negative). This classification facilitates sentiment analysis tasks.
6. Comment: The actual text of the customer's review or comment, conveying specific opinions, feedback, or experiences regarding the product.

Bangla ( Bengali ) sentiment analysis dataset

The repository contains 3307 Negative reviews and 8500 Positive reviews collected and manually annotated from Youtube Bengali drama.

If you use this dataset, please cite the following paper-

@inproceedings{sazzed2020cross, title={Cross-lingual sentiment classification in low-resource Bengali language}, author={Sazzed, Salim}, booktitle={Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)}, pages={50--60}, year={2020} }

If you have any questions, please email me- salimsazzad222@gmail.com.

Bangla ( Bengali ) sentiment analysis dataset

The repository contains 3307 Negative reviews and 8500 Positive reviews collected and manually annotated from Youtube Bengali drama.

If you use this dataset, please cite the following paper-

@inproceedings{sazzed2020cross, title={Cross-lingual sentiment classification in low-resource Bengali language}, author={Sazzed, Salim}, booktitle={Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)}, pages={50--60}, year={2020} }

If you have any questions, please email me- salimsazzad222@gmail.com.

BanglaMUSE is a multimodal Bangla sentiment dataset containing aligned text–audio pairs designed for research in sentiment analysis, speech processing, and multimodal learning for low-resource languages.

The dataset includes 1,000 Bangla sentences, evenly balanced between positive (500) and negative (500) sentiment classes. The sentences represent natural, everyday Bangla language usage and were manually curated and validated to ensure clear sentiment polarity.

Each sentence is recorded by four native Bangla speakers (two female and two male), resulting in 4,000 speech recordings in total. All speakers recorded the same set of sentences, enabling controlled analysis of speaker variability while preserving identical textual content. Audio samples are provided in MP3 format, recorded in controlled indoor environments, and manually verified for quality and alignment.

The dataset is distributed with a unified metadata.csv file that links sentence identifiers, sentiment labels, speaker information, and relative audio paths. BanglaMUSE supports tasks such as multimodal sentiment classification, sentiment-aware speech recognition, audio–text alignment, and speaker-independent modeling.

This is a data set of Sentiment Analysis On Bangla News Comments where every data was annotated by three different individuals to get three different perspectives and based on the majorities decisions the final tag was chosen. This data set contains 13802 data in total.

• Bangla, a language spoken by more than 230 million people worldwide, is significantly underrepresented in speech and sentiment analysis research when compared to high-resource languages. • This is addressed with the dataset. Researchers and developers working on low-resource language technologies, such as sentiment analysis, speech recognition, and multimodal learning frameworks, should find this extensive resource very helpful. • Sentiment-aware speech recognition, speech-based emotion detection, emotionally expressive text-to-speech systems, multimodal sentiment classification, and speaker-independent recognition models are just a few of the many applications that can be developed and evaluated using this dataset. • Its modular structure promotes continuous research expansion by enabling contributors to add new regional vocabularies, dialectal variations, or additional sentiment classes over time. • The dataset is precisely balanced, with 4,000 audio recordings created by four native speakers (two male and two female) and 500 samples for each sentiment category. The sentences capture the natural and everyday use of the Bangla language, spanning a wide range of topics that include events, emotions, personal experiences, and general statements.

Dataset

This dataset was created by Sushmit

Released under GPL 2

Contents

This dataset comprises 3,290 Bengali political comments sourced from social media platforms, news comment sections, and online political discussions, specifically curated for sentiment analysis research in Bengali NLP. The corpus provides a comprehensive resource for training and evaluating sentiment classification models within the political domain. The dataset features 3,290 instances distributed across five sentiment classes with excellent balance (variance <8%): Very Negative (675, 20.5%), Negative (663, 20.2%), Neutral (626, 19.0%), Very Positive (664, 20.2%), and Positive (662, 20.1%). Stored in Excel format with two columns containing Bengali political comments (Unicode text) and corresponding sentiment labels, the dataset maintains high quality with no missing values and verified annotations. Comment lengths average 83 characters, ranging from 11 to 398 characters. The collection encompasses diverse political discourse including government policies and governance, electoral processes and democracy, political parties and leadership dynamics, social and economic issues, current affairs and political events, along with public opinion and citizen responses to political developments. This dataset serves multiple research purposes, including Bengali sentiment analysis model development and benchmarking, political discourse analysis and opinion mining, natural language processing research for low-resource languages, cross-lingual sentiment analysis studies, social media analytics for Bengali content, multi-class text classification research, and comparative political sentiment studies across different linguistic and cultural contexts.

The repository contains 3307 Negative reviews and 8500 Positive reviews collected and manually annotated from Youtube Bengali drama.If you use this dataset, please cite the following paper-@inproceedings{sazzed2020cross,title={Cross-lingual sentiment classification in low-resource Bengali language},author={Sazzed, Salim},booktitle={Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)},pages={50--60},year={2020}

}If you have any questions, please email me- salimsazzad222@gmail.com.

This dataset contains around 1300 positive and negative Bengal ( Bangla ) sentiment words. This lexicon was created from a Bengali review corpus.

If you use this lexicon please cite following paper-

@inproceedings{sazzed2020development, title={Development of Sentiment Lexicon in Bengali utilizing Corpus and Cross-lingual Resources}, author={Sazzed, Salim}, booktitle={2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)}, pages={237--244}, year={2020}, organization={IEEE Computer Society} }

https://www.cs.odu.edu/~ssazzed/IEEE_IRI_2020.pdf

As a result of the technological advancements of the internet, Bangladeshi users are increasingly active on social networks. In this sense, social media influencers are becoming more well-known and attracting a growing number of users. Bangladeshi food review influencers are becoming more and more well-known every day. The most sophisticated Bengali sequence classification model was used in this study's analysis of social network interaction data. Through an extensive exploration of the social media landscape, we delve into the realm of food reviews. We used the sequence classification model to classify the comments collected from social media for our study. Our findings reveal that the majority of viewers hold a positive perception of Bengali food reviews on social media, while a small number of outliers may express contrasting opinions. Notably, our classifier, BanglaBERT, achieves an impressive prediction accuracy of 83.76%, emphasizing the reliability and effectiveness of our approach.

MONOBHAV is a Bengali dataset for fine-grained sentiment analysis. It contains 10,000 Bengali texts collected from social media platforms and newspaper websites. Each text is manually annotated by native Bengali speakers into five sentiment classes - Strongly Negative, Negative, Neutral, Positive, and Strongly Positive. This dataset enhances the resources available for Bengali sentiment analysis and supports the development and evaluation of more accurate sentiment models for the language.

Critical studies found NLP systems to bias based on gender and racial identities. However, few studies focused on identities defined by cultural factors like religion and nationality. Compared to English, such research efforts are even further limited in major languages like Bengali due to the unavailability of labeled datasets. Our paper (see the reference) describes a process for developing a bias evaluation dataset highlighting cultural influences on identity. We also provide this Bengali dataset as an artifact outcome that can contribute to future critical research.

If you find this dataset useful, please cite the associated paper:

Das, D., Guha, S., & Semaan, B. (2023, May). Toward Cultural Bias Evaluation Datasets: The Case of Bengali Gender, Religious, and National Identity. In Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) (pp. 68-83).

BibTeX:

@inproceedings{das-etal-2023-toward, title = "Toward Cultural Bias Evaluation Datasets: The Case of {B}engali Gender, Religious, and National Identity", author = "Das, Dipto and Guha, Shion and Semaan, Bryan", booktitle = "Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.c3nlp-1.8", pages = "68--83", }

In the Bangla language, sentiment analysis is becoming more and more significant. Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity on an aspect level. The data were collected from numerous individuals with a minimum of two aspects. Every comment is a complex or compound sentence. The datasets are organized in a folder named "BANGLA_ABSA dataset" which has four Excel files, one for each of the datasets: Car_ABSA, Mobile_phone_ABSA, Movie_ABSA, and Restaurant_ABSA. Each Excel file contains three columns namely Id, Comment, and {Aspect category, Sentiment Polarity}. Car_ABSA, Mobile_phone_ABSA, Movie_ABSA, and Restaurant_ABSA datasets have 1149, 975, 800, and 801 rows of data respectively.

SentiFive is a multi-class Bengali sentiment analysis dataset consisting of 31,411 YouTube comments, manually annotated into five sentiment categories: Strongly Negative, Weakly Negative, Neutral, Weakly Positive, and Strongly Positive. The dataset is designed to support research in fine-grained sentiment classification and low-resource language processing.

Unlike previous Bengali sentiment datasets that focus on binary or ternary sentiment, SentiFive enables more nuanced modeling of user opinions expressed in informal social media contexts. Data were collected from a diverse set of YouTube videos, covering topics such as news, entertainment, and politics.

Rahman, M. A., Mahbub, A., Paul, B. N., Bhattacharjee, P., & Ashik, M. A.-Z. (2025). SentiFive: A Multi-Class Bengali Dataset for Sentiment Analysis. Accepted for presentation at the IEEE 7th International Conference on Sustainable Technologies for Industry 5.0 (STI 2025), Dhaka, Bangladesh, December 11–12, 2025.

RBE_Sent Dataset Description: The RBE_Sent (Roman Bengali-English Sentiment) dataset is a synthetic, gold-standard code-mixed dataset developed for sentiment analysis tasks involving Romanized Bengali and English. It captures real-world bilingual usage by blending Roman Bengali with English tokens within the same textual instances. The dataset is designed to support research in multilingual natural language processing, especially in the context of low-resource, code-mixed languages. Each… See the full description on the dataset page: https://huggingface.co/datasets/DaliaBarua/RBE_Sent.

Context

This dataset is used in Multilabel sentiment analysis and emotion detection for YouTube comments in different kinds of Bengali videos.

Content

There are two files in the folder. There are might be multiple comments with same text. Also it may be noted that, the comments collected here contain abusive and vulgar words, slangs and personal attack. Therefore, we ensure that all annotators are adults.

Sentiment.csv

Id - Unique id number for the comment. Text - Text of the data Label - 1 (3 class label) or 2 (5 class label) Score - Denotes the polarity of the comment. In three class labelling : 1(positive), 0 (neutral), -1(negative) In three class labelling : 2 (highly positive), 1(positive), 0 (neutral), -1(negative), -2(highly negative) Lan - Language of the comment. EN (English), BN (Bengali), RN (Romanized Bangla) Domain - Category of the video.

Emotion.csv

Id - Unique id number for the comment. Text - Text of the data emotion - Corresponding emotion of the comment. Anger/Joy/Disgust/Fear/Surprise/Sad/None (no emotion found) Lan - Language of the comment. EN (English), BN (Bengali), RN (Romanized Bangla) Domain - Category of the video.

Acknowledgements

If you use the dataset in any research work, please cite the following paper as

N. Irtiza Tripto and M. Eunus Ali, "Detecting Multilabel Sentiment and Emotions from Bangla YouTube Comments," 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, 2018, pp. 1-6.

doi: 10.1109/ICBSLP.2018.8554875

Inspiration

It will be helpful for researchers specially in analyzing sentiments from social media in non-English language