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.

ROOTS Subset: roots_indic-bn_bangla_sentiment_classification_datasets

  Bangla Sentiment Classification Datasets

Dataset uid: bangla_sentiment_classification_datasets

  Description

Multiple sentiment classification datasets for Bengali, which can also be used for training LMs. The Datasets are the following: ABSA_datasets -- This dataset has developed to perform aspect based sentiment analysis task in Bangla. License: CC BY 4.0 SAIL_data -- This dataset, consists of tweet… See the full description on the dataset page: https://huggingface.co/datasets/bigscience-data/roots_indic-bn_bangla_sentiment_classification_datasets.

Bengali-English Code-Mixed Sentiment Dataset

  Dataset Summary

This dataset contains Bengali–English code-mixed social media text annotated for sentiment classification.The primary goal is to support research and applications in code-mixed NLP, especially sentiment analysis in low-resource Indic languages. The dataset combines and cleans multiple publicly available sources:

BnSentMix: Bengali–English code-mixed sentiment dataset
SentMix-3L: Multi-lingual code-mixed… See the full description on the dataset page: https://huggingface.co/datasets/Swarnadeep-28/bn_code_mix_sentiment_dataset.

Dataset

This dataset was created by Nuhash Afnan

Contents

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.

Mahadih534/Bengali-E-commerce-sentiments dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset consists of YouTube comments predominantly collected from political news videos relevant to Bangladesh. The comments are written in Bengali, enriched with emojis that express a range of emotions and opinions. These comments provide unique insights into the public sentiment and reactions related to political events, figures, and policies within the country. This dataset can be highly useful for NLP tasks such as sentiment analysis, emotion detection, and opinion mining. It enables researchers to study public sentiment, emotional expression, and political opinions through text and emojis in Bengali.

Key Features:

Language: The comments are in Bengali, reflecting authentic language use with local expressions and cultural nuances.

Emojis: The presence of emojis in the dataset helps capture non-verbal cues and emotional expressions that add depth to the textual sentiment.

Context: The data is sourced from videos specifically focused on political news, making it valuable for research related to social, political, and media analysis in Bangladesh.

Context

Data Set For Sentiment Analysis On Bengali News Comments

Content

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.

Acknowledgements

https://data.mendeley.com/datasets/n53xt69gnf/2

Inspiration

aiming to improve bengali and romanic bangla nlp works

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

The most recent Natural Language Processing (NLP) method for ascertaining a user's sentiment is sentiment analysis. Online gaming is one of the activities that people of all ages, especially young people, are being forced to engage in as a result of the recent COVID-19 pandemic. Since smartphones have made it easy for people to access the internet, the number of people playing online games has increased. This research study has used various machines learning classification algorithms from over 401 data points in an attempt to investigate online gaming addiction. All age groups are taken into account when gathering data, but students in high school, college, and university are given special consideration.

Data Collection :**

This section identifies the different category of Bengali language. Here, two different parameters are considered. First one is "Class" and another one is "Opinions". The main focus of the proposed model is to get the user feedback on online game addiction, analyze the user data and identify the types of datasets accordingly. As a result, the proposed dataset is restricted to collecting 401 text documents and in only two columns. One is paragraph or text form and another one is classification. Paragraph or text means positive ,negative and neutral on which the class will be labelled on the other side, it means the 'opinions' of user about the online gaming addiction. ----more details of my paper: DOI : http://dx.doi.org/10.1109/I-SMAC55078.2022.9987343

Dataset

This dataset was created by Tazim H

Contents

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.

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.

Synthetic Bengali Sentiment Analysis Dataset

Overview

This dataset contains 44,236 Bengali sentences with corresponding sentiment labels, synthetically generated using ChatGPT. The dataset is designed for Bengali natural language processing tasks, particularly sentiment analysis.

Dataset Information

• Language: Bengali (বাংলা)
• Size: 44,236 entries
• Format: JSON
• Generation Method: Synthetic data created using ChatGPT
• Encoding: UTF-8

Data Structure

Each entry contains: - text: Bengali sentence/phrase - label: Sentiment label (integer)

Label Distribution

• 0: Negative sentiment
• 1: Neutral sentiment
• 2: Positive sentiment

Sample Data

[
 {
  "text": "আজকের দিনটা একদম ভালো যায়নি।",
  "label": 0
 },
 {
  "text": "বাসা থেকে বের হতে দেরি হয়ে গেল।", 
  "label": 0
 },
 {
  "text": "আমার খুব ভাল লাগছে।",
  "label": 2
 }
]


Run Code:
https://www.kaggle.com/code/piketar/bengali-sentiment-analysis

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.

Context

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

Acknowledgements

Sazzed, Salim (2021), “Bangla ( Bengali ) sentiment analysis classification benchmark dataset corpus”, Mendeley Data, V4, doi: 10.17632/p6zc7krs37.4

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.

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 entry in RBE_Sent is annotated for sentiment, enabling supervised learning and evaluation. By providing a structured and labeled resource in this underexplored linguistic domain, RBE_Sent contributes to advancing computational methods for understanding Bengali-English code-mixed communication.

This Dataset contains Bengali matrix codemixed product review text. Dataset has 18086 roman Bengali-English codemixed product reviews .

license: mit language:

bn en tags: codemixed product review ecommerce bengali dataset pretty_name:

Dataset

This dataset was created by Mahfuz Ahmed Masum

Released under CC0: Public Domain

Contents

In recent years, the surge in reviews and comments on newspapers and social media has made sentiment analysis a focal point of interest for researchers. Sentiment analysis is also gaining popularity in the Bengali language. However, Aspect-Based Sentiment Analysis is considered a difficult task in the Bengali language due to the shortage of perfectly labeled datasets and the complex variations in the Bengali language. This study used two open-source benchmark datasets of the Bengali language, Cricket, and Restaurant, for our Aspect-Based Sentiment Analysis task. The original work was based on the Random Forest, Support Vector Machine, K-Nearest Neighbors, and Convolutional Neural Network models. In this work, we used the Bidirectional Encoder Representations from Transformers, the Robustly Optimized BERT Approach, and our proposed hybrid transformative Random Forest and Bidirectional Encoder Representations from Transformers (tRF-BERT) models to compare the results with the existing work. After comparing the results, we can clearly see that all the models used in our work achieved better results than any of the previous works on the same dataset. Amongst them, our proposed transformative Random Forest and Bidirectional Encoder Representations from Transformers achieved the highest F1 score and accuracy. The accuracy and F1 score of aspect detection for the Cricket dataset were 0.89 and 0.85, respectively, and for the Restaurant dataset were 0.92 and 0.89 respectively.