Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

The table Headlines is part of the dataset Daily Financial News, available at https://redivis.com/datasets/97xh-655sbm328. It contains 1845559 rows across 5 variables.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

Get access to leading financial news coverage including exclusive access to Reuters news as well as 10,500 additional news sources and feeds.

KamaleshwariThirumugam/Financial-news dataset hosted on Hugging Face and contributed by the HF Datasets community

Daniel-ML/sentiment-analysis-for-financial-news-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Stay ahead with our comprehensive News Dataset, designed for businesses, analysts, and researchers to track global events, monitor media trends, and extract valuable insights from news sources worldwide.

Dataset Features

News Articles: Access structured news data, including headlines, summaries, full articles, publication dates, and source details. Ideal for media monitoring and sentiment analysis. Publisher & Source Information: Extract details about news publishers, including domain, region, and credibility indicators. Sentiment & Topic Classification: Analyze news sentiment, categorize articles by topic, and track emerging trends in real time. Historical & Real-Time Data: Retrieve historical archives or access continuously updated news feeds for up-to-date insights.

Customizable Subsets for Specific Needs Our News Dataset is fully customizable, allowing you to filter data based on publication date, region, topic, sentiment, or specific news sources. Whether you need broad coverage for trend analysis or focused data for competitive intelligence, we tailor the dataset to your needs.

Popular Use Cases

Media Monitoring & Reputation Management: Track brand mentions, analyze media coverage, and assess public sentiment. Market & Competitive Intelligence: Monitor industry trends, competitor activity, and emerging market opportunities. AI & Machine Learning Training: Use structured news data to train AI models for sentiment analysis, topic classification, and predictive analytics. Financial & Investment Research: Analyze news impact on stock markets, commodities, and economic indicators. Policy & Risk Analysis: Track regulatory changes, geopolitical events, and crisis developments in real time.

Whether you're analyzing market trends, monitoring brand reputation, or training AI models, our News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Dataset

This dataset was created by Rokas Štrimaitis

Released under CC0: Public Domain

Contents

please cite this dataset by :

Nicolas Turenne, Ziwei Chen, Guitao Fan, Jianlong Li, Yiwen Li, Siyuan Wang, Jiaqi Zhou (2021) Mining an English-Chinese parallel Corpus of Financial News, BNU HKBU UIC, technical report

The dataset comes from Financial Times news website (https://www.ft.com/)

news are written in both languages Chinese and English.

The dataset contains 60,473 bilingual documents.

Time range is from 2007 and 2020.

This dataset has been used for parallel bilingual news mining in Finance domain.

Dataset

This dataset was created by Rifath F

Released under Apache 2.0

Contents

Get access to leading investment banking news coverage including exclusive access to Reuters news as well as 10,500 additional news sources and feeds.

Read the biggest business and political stories from around the world with Reuters Top News, providing a customized experience in an easy-to-use format.

@misc{dong2024fnspid, title={FNSPID: A Comprehensive Financial News Dataset in Time Series}, author={Zihan Dong and Xinyu Fan and Zhiyuan Peng}, year={2024}, eprint={2402.06698}, archivePrefix={arXiv}, primaryClass={q-fin.ST} }

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty 50: A Journey to 20,000 and Beyond?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Get access to expert global economic and central banking news coverage including exclusive polls on 700 annual key economic releases and policy decisions.

We provide comprehensive and specialized news coverage of the international banking and finance industries, merging reports from Reuters with expert commentary from IFR. Whether you're on the trading floor or in the executive suite, an investment banker or a wealth manager, Reuters and IFR are the premier sources for news about and for the banking and finance sectors. Reporters from Reuters and IFR offer coverage that is both extensive and in-depth, focusing on corporate strategies, deal-making activities, and regulatory changes that impact the world's top financial institutions. In the realm of corporate finance, the Reuters and IFR teams explore every angle and work around the clock to stay ahead of emerging deals. Reporters, from Brussels to Washington, engage with regulators and lawmakers who have the power to alter the industry's landscape, while also tracking the successes, failures, entries, and exits of major players with speed, accuracy, and sophistication.

asas-ai/financial_news dataset hosted on Hugging Face and contributed by the HF Datasets community

In 2019, the United Arab Emirates (UAE) was mentioned about *** thousand times in the Islamic finance news. In the same year, there were about **** thousand Islamic finance news published as well as *** events which consist of *** conferences and *** seminars. These news and events were part of the Islamic finance awareness campaign.

The Brain Sentiment Indicator monitors public financial news for more than 10000 global stocks from about 2000 financial media sources in 33 languages.

Each stock is assigned a sentiment score ranging from -1 (most negative) to +1 (most positive). Indicators are updated daily and correspond to the average of sentiment for each news article on two time scales; 7 days and 30 days. Volume of news contributing to sentiment scoring is also available upon request, so that weighted rankings could also be derived.

Factsheet https://braincompany.co/assets/files/bsi_summary.pdf

Data dictionary https://braincompany.co/assets/files/bsi_data_dictionary.json

Financial news archive containing 44 summaries across 6 topics.