This statistic presents the annual returns of hedge funds in 2017, by hedge fund type. Equity focused hedge funds performed the best, with the long/short equity funds generating ***** percent and equity market neutral with **** percent returns in that year.

Are hedge funds worth your money?

Hedge funds have developed from investment funds that were designed to lower the risk of your portfolio to a multitude of different investment styles with different goals. Their heyday was probably during the 90s and early 2000s when several star hedge fund managers rose to prominence and their assets under management grew significantly. However, since then hedge funds have been under scrutiny as their investment returns have been lacking and their ability to function as a diversification to a traditional stock and bond portfolio was put into question. As hedge funds have their own set of leverage and investment rules it is no wonder they have been accused of being greedy, unsuccessful and secretive. However, with this dataset you can make your own analysis.

Content

This dataset covers monthly hedge fund returns starting from 1997. The date column refers to the last day of the month - the end date of the return period, if I understand correctly. There are 12 different hedge fund strategies covered and the return index series are formed as an aggregate of other hedge fund index providers.

The strategy explanations are in EDHEC website:

• Convertible Arbitrage - https://risk.edhec.edu/conv-arb/
• CTA Global - https://risk.edhec.edu/cta-global/
• Distressed Securities - https://risk.edhec.edu/dist-sec/
• Emerging Markets - https://risk.edhec.edu/emg-mkts/
• Equity Market Neutral - https://risk.edhec.edu/equity-market-neutral/
• Event Driven - https://risk.edhec.edu/event-driven/
• Fixed Income Arbitrage - https://risk.edhec.edu/fix-inc-arb/
• Global Macro - https://risk.edhec.edu/global-macro/
• Long/Short Equity - https://risk.edhec.edu/ls-equity/
• Merger Arbitrage - https://risk.edhec.edu/merger-arb/
• Relative Value - https://risk.edhec.edu/relative-value/
• Short Selling - https://risk.edhec.edu/short-selling/
• Funds of Funds - https://risk.edhec.edu/fof/

Acknowledgements

All credit for the maintenance and upload of the data goes to EDHEC. You should check their website for additional resources:

https://risk.edhec.edu/all-downloads-hedge-funds-indices

Inspiration

The EDHEC hedge fund data is the data used in examples/vignettes of PortfolioAnalytics - a package for optimizing, testing and analyzing portfolio returns. You should be easily able to expand the analysis from the vignettes just by using the larger dataset available here:

https://cran.r-project.org/web/packages/PortfolioAnalytics/index.html

AXOVISION's low beta signals offer substantial advantages to optimise investment portfolios and can be directly converted into alpha - without any further calculations.

Daily signals, sent at 09:00 EST (15:00 CET) - Build robust strategies with low beta - Universe: S&P500

Strategy: - Selection of top 10 long stocks and top 10 short stocks

Are hedge funds worth your money? Hedge funds have developed from investment funds that were designed to lower the risk of your portfolio to a multitude of different investment styles with different goals. Their heyday was probably during the 90s and early 2000s when several star hedge fund managers rose to prominence and their assets under management grew significantly. However, since then hedge funds have been under scrutiny as their investment returns have been lacking and their ability to function as a diversification to a traditional stock and bond portfolio was put into question. As hedge funds have their own set of leverage and investment rules it is no wonder they have been accused of being greedy, unsuccessful and secretive. However, with this dataset you can make your own analysis.

Content This dataset covers monthly hedge fund returns starting from 1997. The date column refers to the last day of the month - the end date of the return period, if I understand correctly. There are 12 different hedge fund strategies covered and the return index series are formed as an aggregate of other hedge fund index providers.

The strategy explanations are in EDHEC website:

Convertible Arbitrage - https://risk.edhec.edu/conv-arb/ CTA Global - https://risk.edhec.edu/cta-global/ Distressed Securities - https://risk.edhec.edu/dist-sec/ Emerging Markets - https://risk.edhec.edu/emg-mkts/ Equity Market Neutral - https://risk.edhec.edu/equity-market-neutral/ Event Driven - https://risk.edhec.edu/event-driven/ Fixed Income Arbitrage - https://risk.edhec.edu/fix-inc-arb/ Global Macro - https://risk.edhec.edu/global-macro/ Long/Short Equity - https://risk.edhec.edu/ls-equity/ Merger Arbitrage - https://risk.edhec.edu/merger-arb/ Relative Value - https://risk.edhec.edu/relative-value/ Short Selling - https://risk.edhec.edu/short-selling/ Funds of Funds - https://risk.edhec.edu/fof/ Acknowledgements All credit for the maintenance and upload of the data goes to EDHEC. You should check their website for additional resources:

https://risk.edhec.edu/all-downloads-hedge-funds-indices

Inspiration The EDHEC hedge fund data is the data used in examples/vignettes of PortfolioAnalytics - a package for optimizing, testing and analyzing portfolio returns. You should be easily able to expand the analysis from the vignettes just by using the larger dataset available here:

https://cran.r-project.org/web/packages/PortfolioAnalytics/index.html

Index Time Series for Timothy Plan Market Neutral ETF. The frequency of the observation is daily. Moving average series are also typically included. The adviser pursues the fund's investment objective by implementing a proprietary, "market neutral" investment strategy designed to seek income from the fund's investments while maintaining a low correlation to the foreign and domestic equity and bond markets. The adviser uses a multi-strategy approach. The fund will be actively managed, meaning that the sub-advisor may make changes to the fund's portfolio at any time.

Index Time Series for AGFiQ U.S. Market Neutral Anti-Beta Fund. The frequency of the observation is daily. Moving average series are also typically included. The fund will invest primarily in long positions in low beta U.S. equities and short positions in high beta U.S. equities on a dollar neutral basis, within sectors. It will construct a dollar neutral portfolio of long and short positions of U.S. equities by investing primarily in the constituent securities of the Dow Jones U.S. Thematic Market Neutral Low Beta Index in approximately the same weight as they appear in the index. The universe for the index is comprised of the top 1,000 eligible securities by market capitalization, including REITs.

Hedge funds are private, unregulated investment funds that use sophisticated instruments or strategies, such as derivative securities, short positions or leveraging, to generate alpha. Hedge funds cover a wide range of strategies with different risk and return profiles.

About This Dataset

Data Date: 1997/1 - 2021/6 Columns : 13 Different Investing Style Index Value : Monthly Return

Description

Convertible Arbitrage : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/conv_arb.pdf CTA Global : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/cta.pdf Distressed Securities : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/distressed.pdf Emerging Markets : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/emerging.pdf Equity Market Neutral : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/market_ntl.pdf Event Driven : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/event_driven.pdf Fixed Income Arbitrage : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/fix_inc.pdf Global Macro : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/global_macro.pdf Long/Short Equity : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/long_short.pdf Merger Arbitrage : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/merger.pdf Relative Value : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/value.pdf Short Selling : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/short.pdf Funds of Funds : https://risk.edhec.edu/sites/risk/files/indices/Indices/Edhec%20Alternative%20Indices/Web/report/fof.pdf

Credit To

Data Source :EDHEC-Risk Institute Since 2003, EDHEC-Risk Institute has been publishing the EDHEC-Risk Alternative Indices, which aggregate and synthesise information from different index providers, so as to provide investors with representative benchmarks. These indices are computed for thirteen investment styles that represent typical hedge fund strategies. https://risk.edhec.edu/all-downloads-hedge-funds-indices

Non-traditional data signals from social media and employment platforms for EQLS stock analysis

Total Return Equity Swaps Market Outlook

According to our latest research, the global Total Return Equity Swaps market size in 2024 stands at USD 3.8 billion, with a robust CAGR of 7.1% projected through the forecast period. By 2033, the market is expected to reach USD 7.1 billion, reflecting strong momentum driven by increased demand for sophisticated risk management tools and regulatory changes favoring derivative instruments. The growth of the Total Return Equity Swaps market is primarily fueled by the need for flexible investment vehicles, heightened volatility in equity markets, and the rising adoption of derivatives among institutional investors worldwide.

One of the primary growth drivers for the Total Return Equity Swaps market is the increasing sophistication of institutional investors, such as hedge funds, asset managers, and pension funds, who are seeking tailored solutions to manage equity exposure and optimize portfolio returns. The ability of total return equity swaps to provide synthetic exposure to a wide range of underlying assets, including single stocks, indices, and ETFs, without the direct ownership of the assets, allows investors to achieve desired market positions with greater capital efficiency and flexibility. Furthermore, these instruments enable market participants to hedge against market downturns or to gain leverage, which is particularly attractive in periods of heightened market volatility or when direct equity ownership is constrained by regulatory or operational considerations.

Another significant factor propelling the market is the evolving regulatory landscape. Post-2008 financial reforms have increased transparency and standardized the reporting of over-the-counter derivatives, making total return equity swaps more accessible and less risky for a broader range of market participants. The implementation of central clearing and margin requirements has mitigated counterparty risk, encouraging greater participation from both buy-side and sell-side entities. Additionally, the growing demand for customized swap agreements that cater to specific investment strategies, such as long/short equity, market-neutral, and sector rotation, has further expanded the utility and appeal of total return equity swaps in institutional portfolios.

Technological advancements in trading platforms and risk management systems have also played a crucial role in the expansion of the Total Return Equity Swaps market. The integration of advanced analytics, real-time pricing, and automation has streamlined the execution and monitoring of swap transactions, reducing operational costs and enhancing transparency. As financial institutions continue to invest in digital infrastructure, the accessibility and efficiency of total return equity swaps are expected to improve, attracting new participants and supporting market growth. Moreover, the globalization of capital markets and the increasing interconnectedness of regional exchanges are fostering cross-border swap activity, further supporting the upward trajectory of the market.

From a regional perspective, North America remains the dominant market for total return equity swaps, accounting for the largest share of global transactions in 2024, followed by Europe and the Asia Pacific. The presence of sophisticated financial markets, a high concentration of institutional investors, and a favorable regulatory environment in the United States and Canada underpin the regionÂ’s leadership. EuropeÂ’s market is buoyed by the strong presence of global banks and asset managers, while Asia Pacific is witnessing rapid growth driven by financial market liberalization and rising investor sophistication in countries such as China, Japan, and Australia. Latin America and the Middle East & Africa, though smaller in scale, are emerging as promising markets due to increasing cross-border capital flows and the gradual adoption of advanced financial instruments.

Commodity Swaps are another critical component in the derivatives market, offering investors a mechanism to hedge against price fluctuations in various commodities such as oil, natural gas, and agricultural products. These swaps allow parties to exchange cash flows based on the price of a specific commodity, providing a way to stabilize income and manage risk associated with volatile

This dataset contains 2000 daily stock market records including price movements, trading volume, market trends, indices, economic scores, and market sentiment information. It covers multiple sectors with a general category column and includes a target column for the next-day closing price. Additional text columns capture market sentiment and news tags for each record. The dataset is designed to provide comprehensive insights into stock market behavior and trends.

Number of Records: 2000

Number of Columns: 18

Column Descriptions:

Category – General text representing the sector or type of stock (e.g., Tech, Finance, Health).

Date – The calendar date of the stock record.

Open – The opening price of the stock on that day.

High – The highest price of the stock during the day.

Low – The lowest price of the stock during the day.

Close – The closing price of the stock on that day.

Volume – The total number of shares traded during the day.

SMA_10 – The 10-day simple moving average of the closing price, showing short-term trend.

EMA_10 – The 10-day exponential moving average of the closing price, giving more weight to recent prices.

Volatility – The standard deviation of the closing price over a 10-day window, representing price fluctuation.

Wavelet_Trend – Trend component of the closing price over a 10-day period.

Wavelet_Noise – Difference between the actual closing price and the trend component, capturing minor fluctuations.

Wavelet_HighFreq – Daily price changes in closing price, showing high-frequency movement.

General_Index – A numeric indicator representing general market performance.

Economic_Score – A numeric score representing overall economic factors impacting the stock.

Market_Sentiment – Text describing the sentiment of the market for that day (Positive, Neutral, Negative).

News_Tag – Text describing the main type of news impacting the stock on that day (e.g., Earnings, Merger).

Close_Next – The closing price of the stock for the next day, serving as the target variable.

Non-traditional data signals from social media and employment platforms for COGMX stock analysis

Financial News Events Dataset - Comprehensive Description

Overview

This synthetic dataset contains 3,024 records of financial news headlines centered around major market events from February 2025 to August 2025. The dataset captures real-time market dynamics, sentiment analysis, and trading patterns across global financial markets, making it ideal for financial analysis, sentiment modeling, and market prediction tasks.

Dataset Specifications

• Total Records: 3,024 rows
• Total Features: 12 columns
• Date Range: February 1, 2025 - August 14, 2025
• File Formats: CSV, JSON, XLSX
• Data Quality: ~5% null values strategically distributed for realistic data cleaning scenarios

Column Descriptions

Key Features & Statistics

Market Events Coverage (20 Categories)

• Stock Market Crashes & Rallies
• Interest Rate Changes & Central Bank Meetings
• Corporate Earnings Reports & IPO Launches
• Government Policy Announcements
• Trade Tariffs & Geopolitical Events
• Cryptocurrency Regulations
• Supply Chain Disruptions
• Economic Data Releases

Global Market Indices (18 Major Indices)

• US Markets: S&P 500, Dow Jones, Nasdaq Composite, Russell 2000
• Indian Markets: NSE Nifty, BSE Sensex
• European Markets: FTSE 100, DAX, Euro Stoxx 50, CAC 40
• Asian Markets: Nikkei 225, Hang Seng, Shanghai Composite, KOSPI
• Others: TSX, ASX 200, IBOVESPA, S&P/TSX Composite

News Sources (18 Reputable Publications)

Major financial news outlets including Reuters, Bloomberg, CNBC, Financial Times, Wall Street Journal, Economic Times, Forbes, and specialized financial publications.

Sector Distribution (18 Business Sectors)

Technology, Finance, Healthcare, Energy, Consumer Goods, Utilities, Industrials, Materials, Real Estate, Telecommunications, Automotive, Retail, Pharmaceuticals, Aerospace & Defense, Agriculture, Transportation, Media & Entertainment, Construction.

Data Quality & Preprocessing Notes

• Realistic Null Distribution: Approximately 5% null values in key columns (Headline, Sentiment, Index_Change_Percent, News_Url) to simulate real-world data collection challenges
• Balanced Sentiment Distribution: Mix of positive, neutral, and negative sentiment classifications
• Diverse Market Conditions: Index changes ranging from -5% to +5% reflecting various market scenarios
• Volume Variability: Trading volumes span 1M to 500M to represent different market liquidity conditions

Potential Use Cases

📈 Financial Analysis

• Market sentiment analysis and trend prediction
• Correlation studies between news events and market movements
• Trading volume pattern analysis

🤖 Machine Learning Applications

• Sentiment classification model training
• Market movement prediction algorithms
• News headline generation models
• Event-driven trading strategy development

📊 Data Visualization Projects

• Interactive market sentiment dashboards
• Time-series analysis of market events
• Geographic distribution of financial news impact
• Sector-wise performance visualization

🔍 Research Applications

• Academic research on market efficiency
• News impact analysis on different sectors
• Cross-market correlation studies
• Event study methodologies

Technical Specifications

• Memory Usage: Approximately 1.5MB across all formats
• **Proces...

Common-Stock Time Series for Virtu Financial, Inc.. Virtu Financial, Inc. operates as a financial services company in the United States, Ireland, and internationally. It operates through two segments: Market Making and Execution Services. The company's product includes offerings in execution, liquidity sourcing, analytics and broker-neutral, capital markets, and multi-dealer platforms in workflow technology. Its product allows its clients to trade on various venues across 50 countries and in various asset classes, including global equities, ETFs, options, foreign exchange, futures, fixed income, cryptocurrencies, and myriad other commodities. The company's multi-asset analytics platform provides a range of pre- and post-trade services, data products, and compliance tools for clients to invest, trade, and manage risk across global markets. Virtu Financial, Inc. was founded in 2008 and is headquartered in New York, New York.

FinBERT Training Dataset Card

  Dataset Details

Dataset Name: Indian Stock Market Sentiment Dataset Purpose: Fine-tuning FinBERT for sentiment analysis of Indian financial news and reports. Task: Text classification / Sentiment analysis Languages: English Labels: POSITIVE, NEUTRAL, NEGATIVE

  Dataset Usage

import pandas as pd

Load dataset

df = pd.read_csv('indian_stock_sentiment.csv')

Inspect examples

print(df.head())

This dataset can be used to fine-tune… See the full description on the dataset page: https://huggingface.co/datasets/harixn/indian_news_sentiment.

Context

The stock and financial market is of great importance to many. News about the stock market can provide an interesting overview of how companies of current events are percieved. With this dataset, you could build a classifier that can differentiate between positive, neutral or bad stock news. Please be aware that this dataset is only meant for educational purposes and does not intent to be investment advice in any way.

Content

The dataset is strucktured as follows: - headline: Headline of an article about stocks or a company - label: Either Positive, Neutral or Negative

Acknowledgements

The stock news were gathered via the website finviz.com.

Inspiration

Are there any errors in this dataset? What would you do with a stock news classifier?

I- Overview:

NifSent50 is a comprehensive India-centric sentiment dataset designed for NIFTY 50 stock market analysis. It links company-specific news and social media posts to subsequent stock price movements, enabling sentiment classification tuned to the Indian market.

Why This Dataset Is Different

Unlike typical NLP sentiment datasets that label text as positive or negative based on linguistic tone, NifSent50 measures sentiment through actual market reaction. Each headline or post is correlated with subsequent stock price movement, reflecting how the market interpreted and responded to that information. This makes it a behaviorally grounded dataset, capturing real investor sentiment rather than textual polarity alone.

Context:

Unlike Western markets, the Indian stock market is based more on sentiment than on numbers, where news, politics, and cultural narratives can influence investor behavior. Optimism, fear, and risk-taking shaped by uniquely Indian cues often defy foreign-trained models, making this market a class of its own.

II- Dataset Details

1. Companies Covered: All NIFTY 50 stocks
2. Sources: NewsAPI, Investing.com, Reddit (r/WorldNews, r/news, r/IndiaNews)
3. Stock Data: Yahoo Finance (manual yearly merges for full historical coverage)

Sentiment Labels:

• Positive → Stock increased > 1% after news/post
• Negative → Stock decreased > 1%
• Neutral → Price change within ±1% or insufficient forward data

III- Data Construction

The dataset was built using three integrated pipelines:

1. NewsAPI + Yahoo Finance

• Headlines fetched via NewsAPI for each company.
• Stock prices sourced from Yahoo Finance (manual yearly merges).
• Sentiment calculated from post-news price changes.

Note: While Yahoo Finance data is freely available, it can only be downloaded one year at a time. For this dataset, yearly CSV files were individually retrieved and then manually merged to create a continuous multi-year stock history for all NIFTY 50 companies. This process ensures complete temporal coverage for accurate sentiment labeling.

2. Investing.com Scraper

• Headlines scraped using Selenium + BeautifulSoup.
• Dates aligned with NIFTY 50 stock history.
• Sentiment derived from two-day percentage change in stock price.

3. Reddit Scrapers

• Posts from subreddits: r/WorldNews, r/news, r/IndiaNews.
• Company mentions mapped via Info.csv.
• Sentiment assigned based on stock movement after publication.

Context

This corpus was created during my PhD research at the Institute of Computing, from University of Campinas under supervision of Professors Ariadne Carvalho and Norton Roman. It consists on a crowd-sourcing experiment for annotating emotions on tweets related to the Brazilian stock market.

Content

We made available the following output from our annotation system:

tweets_annotators.csv: A list of all annotators and their profiles (without personal information)

tweets.csv: A list of all tweets available for annotation

tweets_annotations.csv: All the individual annotations with emotions, also indicating the annotator and the tweet id and the annotation date and time

tweets_stocks.csv: The final corpus with annotations after considering the majority of annotators, containing only tweets with at least 3 annotations

For each emotion column, the value 0 indicates neutral (ie, the absence of that emotion) and an emotion marked with 1 indicates the presence of the emotion. Tweets marked 0 on all emotions are considered neutral.

The value -1 indicates that most annotators marked that they "don't know how to respond" for that pair of emotions (eg, they can't tell if the tweet indicates happiness or sadness) and the value -2 indicates that there was a tie in a given pair of emotions (eg, 1 annotator marked joy, 1 annotator marked sadness, and 1 annotator marked neutral). There are no other negative values.

In both cases, the tweet cannot be considered for a classifier that uses that emotion. If you use binary classifiers for each emotion pair, so a tweet marked as negative annotation in happiness and sadness may still be considered for a fear vs. anger classifier, if there are valid values ​​for those emotions, for example. If using a multi-class classifier, I believe you might consider ignoring these tweets, as there is no way to tell if these emotions are present in them.

tweets_stocks-full_agreement.csv: A tweets_stocks.csv subset containing only tweets annotated by at least 3 people, in which all of them agreed upon the emotions or only one marked as "don't know"

Three news sources have been used in creating this dataset. 1. Sun, J. (2016, August). Daily News for Stock Market Prediction, Version 1. Retrieved (2024, August) from https://www.kaggle.com/aaron7sun/stocknews. 2. ARYAN SINGH. NYT Articles: 2.1M+ (2000-Present) Daily Updated. https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present. 3. GABRIEL PREDA. BBC News. https://www.kaggle.com/datasets/gpreda/bbc-news.

The first source covers from 2008-06-08 to 2016-07-01, the top 25 news of each day from Reddit World News. The second source is a direct import of the abstract column from New York Times articles from 2016-07-01 to 2017-09-05. The third is also a direct import of the description column from BBC News from 2017-09-05 to 2024-08-03. Thus, the whole coverage is from 2008-06-08 to 2024-08-03.

Three models have been used for sentiment results. NLTK VADER is applied first as it is the most lightweight and fastest to apply on large amounts of data. But, as news is mostly neural, NLTK vader gave a 1.0 neutral score for around 25% of the data. Therefore, two more advanced models, NLTK RoBERTa and HUGGING FACE distilbert-base-uncased-finetuned-sst-2-english, are applied to these neutral articles to identify them accurately.

Part of my school project for Nanyang Polytechnic | AI & Data Engineering

Dataset Name: BBC Articles Sentiment Analysis Dataset

Source: BBC News

Description: This dataset consists of articles from the BBC News website, containing a diverse range of topics such as business, politics, entertainment, technology, sports, and more. The dataset includes articles from various time periods and categories, along with labels representing the sentiment of the article. The sentiment labels indicate whether the tone of the article is positive, negative, or neutral, making it suitable for sentiment analysis tasks.

Number of Instances: [Specify the number of articles in the dataset, for example, 2,225 articles]

Number of Features: 1. Article Text: The content of the article (string). 2. Sentiment Label: The sentiment classification of the article. The possible labels are: - Positive - Negative - Neutral

Data Fields: - id: Unique identifier for each article. - category: The category or topic of the article (e.g., business, politics, sports). - title: The title of the article. - content: The full text of the article. - sentiment: The sentiment label (positive, negative, or neutral).

Example: | id | category | title | content | sentiment | |----|-----------|---------------------------|-------------------------------------------------------------------------|-----------| | 1 | Business | "Stock Market Surge" | "The stock market has surged to new highs, driven by strong earnings..." | Positive | | 2 | Politics | "Election Results" | "The election results were a mixed bag, with some surprises along the way." | Neutral | | 3 | Sports | "Team Wins Championship" | "The team won the championship after a thrilling final match." | Positive | | 4 | Technology | "New Smartphone Release" | "The new smartphone release has received mixed reactions from users." | Negative |

Preprocessing Notes: - The text has been preprocessed to remove special characters and any HTML tags that might have been included in the original articles. - Tokenization or further text cleaning (e.g., lowercasing, stopword removal) may be necessary depending on the model and method used for sentiment classification.

Use Case: This dataset is ideal for training and evaluating machine learning models for sentiment classification, where the goal is to predict the sentiment (positive, negative, or neutral) based on the article's text.