Omkar2118/datasets huggingface dataset:
TwBNT

TwBNT is the bot detection benchmark with automatic troll annotations.

Introducing paper: SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter
StickerConv

A multimodal empathetic dialogue dataset.

Introducing paper: STICKERCONV: Generating Multimodal Empathetic Responses from Scratch
DATA-PHM

🔍 About data: Our database is designed to be a key tool in the advancement of practical research and application in rotating machinery monitoring. It covers a variety of operating conditions and fault types, making the data applicable to a wide range of industrial scenarios.

🛠 Python Package: This database is provided with a Python package, developed by a P.hD Khaled Benaggoune, which automates the downloading of data, the loading of metadata, and preparing the training and testing splits. This tool is designed to save researchers valuable time, enabling them to concentrate on developing and deploying conditional monitoring algorithms.

💡 Objective: You are invited to review the paper via the following DOI link https://doi.org/10.36001/ijphm.2023.v14i2.3497 to find out more about the data content and the remote access links. Also, the associated codes for the preparation of each database, to be used in your own research and development projects, are available on the following Github: https://github.com/khaledbenag/machinery.

📝 Below the DOI links of the papers that used these data : https://doi.org/10.1016/j.measurement.2019.03.065 ; https://doi.org/10.1016/j.ymssp.2020.106680 ;  https://doi.org/10.1007/s10845-022-01919-y

Introducing paper: Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions
AlbNews

* a corpus for topic modeling in Albanian

Introducing paper: AlbNews: A Corpus of Headlines for Topic Modeling in Albanian
StockEmotions

This repository contains a financial-domain-focused dataset for financial sentiment/emotion classification and stock market time series prediction. It's based on our paper: StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series accepted by AAAI 2023 Bridge (AI for Financial Services).

- Data collection period: Jan 2020 - Dec 2020
- Number of Utterance: 10,000 (train 80%, val 10%, test 10%)
- Sentiment classes: 2 [bullish (~positive), bearish (~negative)]
- Emotion classes: 12 [ambiguous, amusement, anger, anxiety, belief, confusion, depression, disgust, excitement, optimism, panic, surprise]

- **tweet/processed.csv**: 50,281 samples with text-processed data for Topic Modelling
- **tweet/train, val, test.csv**: 10,000 samples in total. Each file has id, date, ticker, emo_label, senti_lable, original, and processed content. For the data curation, processing (e.g. emoji, CTAG, HTAG), and annotation, we refer to our paper. The dataset is used for Financial Sentiment/Emotion Classification tasks.
- **price/38 companies**: historical price data in csv format. The tweet and price dataset together are used for Multivariate Time Series tasks.

Introducing paper: StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series
AlbNER

* Named Entity Recognition in Albanian

Introducing paper: AlbNER: A Corpus for Named Entity Recognition in Albanian
AlbMoRe

* Sentiment Analysis of Movie Reviews in Albanian

Introducing paper: AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian