datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NLU-Sentiment-Analysis
SEA Sentiment Analysis
SEA Sentiment Analysis evaluates a model's ability to identify the sentiment polarity of a text. It is sampled from NusaX for Indonesian, Javanese, and Sundanese, IndicSentiment for Tamil, Wisesight Sentiment for Thai, and UIT-VSFC for Vietnamese.
Supported Tasks and Leaderboards
SEA Sentiment Analysis is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/NLU-Sentiment-Analysis.turkish-sentiment-analysis-dataset
Dataset
This dataset contains positive , negative and notr sentences from several data sources given in the references. In the most sentiment models , there are only two labels; positive and negative. However , user input can be totally notr sentence. For such cases there were no data I could find. Therefore I created this dataset with 3 class. Positive and negative sentences are listed below. Notr examples are extraced from turkish wiki dump. In addition, added some random text… See the full description on the dataset page: https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset.amazon-reviews-sentiment-analysis
Dataset Card for amazon reviews for sentiment analysis
Dataset Summary
One of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/amazon-reviews-sentiment-analysis.snappfood-sentiment-analysisdigikala-sentiment-analysismulticlass-sentiment-analysis-dataset
Dataset Card for Dataset Name
Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/Sp1786/multiclass-sentiment-analysis-dataset.sentiment_analysisSENTIPOLC 2016 dataset
The SENTIPOLC 2016 dataset contains 9410 tweets annotated for subjectivity, overall and literal polarity, and irony.
The dataset has been created and used in the context of the SENTIPOLC 2016 task (http://www.di.unito.it/~tutreeb/sentipolc-evalita16/index.html), organized as part of the EVALITA 2016 evaluation campaign.
Original files available here:
https://live.european-language-grid.eu/catalogue/corpus/7479/download/
If you find this dataset useful please cite:… See the full description on the dataset page: https://huggingface.co/datasets/evalitahf/sentiment_analysis.Google-Play-Reviews-for-Sentiment-AnalysisSentimentAnalysisHindi
SentimentAnalysisHindi
An MTEB dataset
Massive Text Embedding Benchmark
Hindi Sentiment Analysis Dataset
Task category
t2c
Domains
Reviews, Written
Reference
https://huggingface.co/datasets/OdiaGenAI/sentiment_analysis_hindi
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("SentimentAnalysisHindi")
evaluator = mteb.MTEB([task])
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/SentimentAnalysisHindi.saraiki-sentiment-analysis-datasetsentiment_analysis_preprocessed_datasetBrief idea about dataset:
This dataset is designed for a Text Classification to be specific Multi Class Classification, inorder to train a model (Supervised Learning) for Sentiment Analysis.
Also to be able retrain the model on the given feedback over a wrong predicted sentiment this dataset will help to manage those things using Other Features.
Main Features
text
labels
This feature variable has all sort of texts, sentences, tweets, etc.
This target variable contains 3 types of… See the full description on the dataset page: https://huggingface.co/datasets/prasadsawant7/sentiment_analysis_preprocessed_dataset.twitter-sentiment-analysisThe Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment.
The dataset is based on data from the following two sources:
University of Michigan Sentiment Analysis competition on Kaggle
Twitter Sentiment Corpus by Niek Sanders
Finally, I randomly selected a subset of them, applied a cleaning process, and divided them between the test and train subsets, keeping a balance between
the number of positive and negative tweets within each of these subsets.sentiment-analysis-in-commodity-market-gold
Dataset Card for Sentiment Analysis of Commodity News (Gold)
This is a news dataset for the commodity market which has been manually annotated for 10,000+ news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021).
The dataset was curated by Ankur Sinha and Tanmay Khandait and is detailed in their paper "Impact of News on the Commodity Market: Dataset and Results." It is currently published by the authors on… See the full description on the dataset page: https://huggingface.co/datasets/SaguaroCapital/sentiment-analysis-in-commodity-market-gold.bengali_sentiment_analysis
Bengali Sentiment Analysis
Context
The dataset contains 3307 Negative reviews and 8500 Positive reviews collected and manually annotated from Youtube Bengali drama.
Positive_Label=1 and Negative_Label=0
Acknowledgements
Sazzed, Salim (2021), “Bangla ( Bengali ) sentiment analysis classification benchmark dataset corpus”, Mendeley Data, V4, doi: 10.17632/p6zc7krs37.4
uzbek-sentiment-analysis
uzbek-sentiment-analysis
Sentiment analysis in the Uzbek language and new Datasets of Uzbek App reviews for Sentiment Classification
Feel free to use the dataset and the tools presented in this project, a paper about more details on creation and usage here.
If you find it useful, please make sure to cite the paper:
@inproceedings{kuriyozov2019deep,
author = {Kuriyozov, Elmurod and Matlatipov, Sanatbek and Alonso, Miguel A and Gómez-Rodríguez, Carlos},
title = {Deep… See the full description on the dataset page: https://huggingface.co/datasets/elmurod1202/uzbek-sentiment-analysis.Turkish_SentimentAnalysis_TRSAv1TRSAv1 (Turkish Sentiment Analysis Version 1) Dataset
This data set has been produced to contribute to Turkish NLP studies.
The dataset consists of a total of 150 thousand samples, 50 thousand negative, 50 thousand positive, and 50 thousand neutral.
It can be used in text classification and sentiment analysis studies by citing the related study.
Related Work
Aydoğan M, Kocaman V. TRSAv1: A new benchmark dataset for classifying user reviews on Turkish e-commerce websites. Journal of… See the full description on the dataset page: https://huggingface.co/datasets/maydogan/Turkish_SentimentAnalysis_TRSAv1.aspect-based-sentiment-analysis-uzbekAmazon_Reviews_for_Sentiment_Analysis_fine_grained_5_classes
Dataset Card for Dataset Name
The Amazon reviews full score dataset is constructed by randomly taking 600,000 training samples and 130,000 testing samples for each review score from 1 to 5. In total there are 3,000,000 trainig samples and 650,000 testing samples.
Dataset Details
Dataset Description
The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 5)… See the full description on the dataset page: https://huggingface.co/datasets/yassiracharki/Amazon_Reviews_for_Sentiment_Analysis_fine_grained_5_classes.portuguese_sentiment_analysisThis dataset is based on the dataset originally posted in Kaggle
Amazon_Reviews_Binary_for_Sentiment_Analysis
Dataset Card for Dataset Name
The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. In the dataset, class 1 is the negative and class 2 is the positive. Each class has 1,800,000 training samples and 200,000 testing samples.
Dataset Details
Dataset Description
The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3… See the full description on the dataset page: https://huggingface.co/datasets/yassiracharki/Amazon_Reviews_Binary_for_Sentiment_Analysis.sentiment-analysis-pt
Sentiment Analysis PT (Teeny-Tiny Castle)
This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.
How to Use
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/sentiment-analysis-pt", split = 'train')
weibo_sentiment_analysis_1000ksentiment-analysis-ind-classification
SentimentAnalysis_ind_Classification
Deduplicated copy of kornwtp/sentiment-analysis-ind-classification.
Splits
split
rows
train
10,082
twitter_sentiment_analysissentiment-analysis
Sentiment Analysis (Teeny-Tiny Castle)
This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.
How to Use
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/sentiment-analysis", split = 'train')
sentiment-analysis-for-financial-news-v2sentiment_analysis_hindiTweets-Sentiment-Analysis
🐦 Tweets-Sentiment-Analysis (bdstar/Tweets-Sentiment-Analysis)
🧠 Overview
A refined and merged version of Tweets text sentiment datasets, providing a clean and well-balanced dataset for sentiment classification across three sentiment categories:positive, negative, and neutral.
This dataset is split into three parts — train, test, and validation — each sourced from highly reputable open datasets.It is designed for training, evaluating, and benchmarking NLP models… See the full description on the dataset page: https://huggingface.co/datasets/bdstar/Tweets-Sentiment-Analysis.sentiment_analysis_hindiConventions followed to decide the polarity: -
labels consisting of a single value are left undisturbed, i.e. if label = 'pos', then it'll be pos
labels consisting of multiple values separated by '&' are processed. If all the labels are the same ('pos&pos&pos' or 'neg&neg'), then the shortened form of the multiple label is assigned as the final label. For example, if label = 'pos&pos&pos', then final label will be 'pos'.
labels consisting of mixed values ('pos&neg&pos' or 'neg&neu&pos') are… See the full description on the dataset page: https://huggingface.co/datasets/OdiaGenAI/sentiment_analysis_hindi.synthetic-persian-chatbot-conversational-sentiment-analysis-anger
Dataset Summary
Synthetic Persian Chatbot Conversational SA – Anger is a Persian (Farsi) dataset created for the Classification task, with a focus on detecting the emotion "anger" in chatbot conversations. It is part of the FaMTEB (Farsi Massive Text Embedding Benchmark). The dataset was synthetically generated using GPT-4o-mini and is derived from the broader Synthetic Persian Chatbot Conversational Sentiment Analysis dataset.
Language(s): Persian (Farsi)
Task(s): Classification… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/synthetic-persian-chatbot-conversational-sentiment-analysis-anger.