Financial Sentiment Analysis Dataset

  Overview

This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.

  Data Description

The dataset comprises… See the full description on the dataset page: https://huggingface.co/datasets/TimKoornstra/financial-tweets-sentiment.

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.

Analysis of ‘Financial Sentiment Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sbhatti/financial-sentiment-analysis on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Data

The following data is intended for advancing financial sentiment analysis research. It's two datasets (FiQA, Financial PhraseBank) combined into one easy-to-use CSV file. It provides financial sentences with sentiment labels.

Citations

Malo, Pekka, et al. "Good debt or bad debt: Detecting semantic orientations in economic texts." Journal of the Association for Information Science and Technology 65.4 (2014): 782-796.

--- Original source retains full ownership of the source dataset ---

FiQA2018-256-24-gpt-4o-2024-05-13-780826 Dataset

  Dataset Description

The dataset "financial sentiment and QA analysis" is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks.

  Associated Model

This dataset was used to train the FiQA2018-256-24-gpt-4o-2024-05-13-780826 model.

  How to Use

To use this dataset for model training or evaluation, you can load it using the Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-780826.

Dataset

This dataset was created by Shaurya Nandecha

Contents

Market sentiment data provides a glimpse into investor perceptions and emotions driving market movements. Understand whether sentiments are bullish, bearish, or neutral, and use these insights to fine-tune your trading decisions.

1. Explore an extensive database of global market indices.
2. 100+ market indices worldwide, from major stock markets to niche sectors.
3. Informed trading & capitalizing on prevailing market trends.
4. A deeper understanding of market sentiment dynamics.
5. Leverage a custom API for over 70,000 tickers, including major FX, commodities, topics, and individuals.
6. Benchmark against real-time market sentiment indicators.
7. 360° Market Dynamics on comprehensive stock market sentiment data.

Mold the dataset to match needs and seamlessly integrate it into various workflows. Count on InfoTrie's proven expertise to deliver accurate and custom stock market data for market analysis.

Utilize sentiment data to amplify strategy, gain a competitive edge, and make confident trading choices. With InfoTrie Stock Market Sentiment Data, you possess the key to unlocking market insights like never before.

Contact us now.

More information on : https://infotrie.com/sentiment-analysis/

Enhancing Financial Market Predictions: Causality-Driven Feature Selection This paper introduces FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset’s extensive coverage spans 15 years from 2007 to 2023 with temporal information, offering a rich, global perspective 160,000 records on financial market news. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability.

Our FinSen Dataset

This repository contains the dataset for Enhancing Financial Market Predictions: Causality-Driven Feature Selection, which has been accepted in ADMA 2024.

If the dataset or the paper has been useful in your research, please add a citation to our work:

@article{liang2024enhancing, title={Enhancing Financial Market Predictions: Causality-Driven Feature Selection}, author={Liang, Wenhao and Li, Zhengyang and Chen, Weitong}, journal={arXiv e-prints}, pages={arXiv--2408}, year={2024} }

Datasets [FinSen] can be downloaded manually from the repository as csv file. Sentiment and its score are generated by FinBert model from the Hugging Face Transformers library under the identifier "ProsusAI/finbert". (Araci, Dogu. "Finbert: Financial sentiment analysis with pre-trained language models." arXiv preprint arXiv:1908.10063 (2019).)

We only provide US for research purpose usage, please contact w.liang@adelaide.edu.au for other countries (total 197 included) if necessary.

We also provide other NLP datasets for text classification tasks here, please cite them correspondingly once you used them in your research if any.

20Newsgroups. Joachims, T., et al.: A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In: ICML. vol. 97, pp. 143–151. Citeseer (1997) AG News. Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015) Financial PhraseBank. Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology 65(4), 782–796 (2014)

Dataloader for FinSen We provide the preprocessing file finsen.py for our FinSen dataset under dataloaders directory for more convienient usage.

Models - Text Classification

DAN-3.

Gobal Pooling CNN.

Models - Regression Prediction

LSTM

Using Sentiment Score from FinSen Predict Result on S&P500 Dependencies The code is based on PyTorch under code frame of https://github.com/torrvision/focal_calibration, please cite their work if you found it is useful.

:smiley: ☺ Happy Research !

S&P Global developed and patented solution that provides daily and quantifiable time series sentiment on the China market.

The Sentiment Analysis Software Market is projected to grow at 18.1% CAGR, reaching $5.83 Billion by 2029. Where is the industry heading next? Get the sample report now!

Dataset

This dataset was created by Sayan Roy

Released under CC0: Public Domain

Contents

Gain data-driven insights for informed investment decisions. Access market sentiment data since 2013 and customize the API for seamless integration. Maximize your stock market understanding with comprehensive analytics on global stock indices, and public and private companies. Analyze sentiment trends and investor behavior with confidence.

Sample Dataset - Historical News Sentiment data for your reference.

Key Features:

1. Tick-by-Tick Sentiment: Access detailed market dynamics with tick-by-tick sentiment data.
2. Custom API: Request a customizable API covering over 70,000 tickers, including major FX, commodities, topics, and people.
3. Proven Expertise: Trust our track record since 2013 for historical data on long-term sentiment patterns.
4. Uncover Hidden Insights: Gauge investor sentiment and reveal market opportunities with the custom API.
5. Real-Time Benchmarks: Enhance your strategies with real-time sentiment indicators.
6. Customizable and Flexible Delivery: Tailor the dataset to your requirements and integrate seamlessly into your workflows.

Gain a competitive edge with InfoTrie's Historical Tick-by-Tick Stock Market Sentiment Data. Request access now to elevate your investment strategies and make data-driven decisions.

More information on : https://infotrie.com/sentiment-analysis/

The global sentiment analytics market was valued at USD 3.15 billion in 2021 and is expected to grow at a CAGR of 14.4% during the forecast period.

Snapshot img

What is the Sentiment Analytics Software Market Size?

The sentiment analytics software market size is forecast to increase by USD 2.34 billion, at a CAGR of 16.6% between 2024 and 2029. The market is experiencing significant growth due to the increasing use of social media and the rising internet penetration in North America. Businesses are leveraging sentiment analysis to gain insights into customer opinions and feedback. A key trend in the market is the integration of generative AI to improve the accuracy and context-dependence of sentiment analysis. However, challenges such as context-dependent errors and the need for large amounts of data to train AI models persist. To stay competitive, market participants must focus on addressing these challenges and continuously improving the accuracy and reliability of their sentiment analysis solutions. This market analysis report provides an in-depth examination of the growth drivers, trends, and challenges shaping the sentiment analytics software market.

What will be the size of Market during the forecast period?

Request Free Sentiment Analytics Software Market Sample

Market Segmentation

The market report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.

Deployment

  On-premises
  Cloud-based


End-user

  Retail
  BFSI
  Healthcare
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

Which is the largest segment driving market growth?

The on-premises segment is estimated to witness significant growth during the forecast period. In the realm of data analysis, sentiment analytics software plays a pivotal role in understanding public perception toward brands, services, and entities. For organizations in the healthcare sector, reputation management is of utmost importance. Sentiment analytics software deployed on-premises offers several benefits. With on-premises deployment, organizations retain complete control over their data, ensuring privacy and compliance with healthcare regulations. This setup allows for customization to meet specific business needs and seamless integration with existing systems.

Get a glance at the market share of various regions. Download the PDF Sample

The on-premises segment was valued at USD 788.40 million in 2019. Furthermore, the use of dedicated infrastructure results in superior performance and faster processing times. Government institutions, media, telecom, and other industries also reap the benefits of on-premises sentiment analytics software. Data from surveys, social media, and other sources undergoes text analysis to uncover valuable insights. By staying informed of public sentiment, organizations can make data-driven decisions, respond to crises, and improve their offerings. Sentiment analysis is not limited to text data from surveys and social media. Media mentions and customer interactions through phone and email are also valuable sources of data. By harnessing the power of on-premises sentiment analytics software, organizations can gain a competitive edge and maintain a strong reputation.

Which region is leading the market?

For more insights on the market share of various regions, Request Free Sample

North America is estimated to contribute 38% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. In North America, sentiment analytics software has gained significant traction due to the region's high internet penetration and prioritization of enhancing customer experiences. By 2024, internet usage in North America reached nearly 97%, creating a solid base for the implementation of sentiment analysis tools. Companies in the US and Canada are investing heavily in advanced technologies to personalize customer interactions and improve overall satisfaction.

Further, Natural Language Processing (NLP) plays a crucial role in sentiment analysis, enabling businesses to understand and respond effectively to customer opinions. By staying attuned to customer sentiments, North American businesses can foster brand reputation, enhance customer satisfaction, and make data-driven decisions.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

Alphabet Inc.: The company offers sentiment analytics software that supports multiple languages and can be integrated into various applications for real-time analysis.

The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure. Contact at Tilburg University: Francesco Lelli Detailed description of the dataset in the file associated to this release

CryptoSentiment is a dataset, which contains sentiment information about cryptocurrency assets, gathered by various online sources, and analyzed by FinBERT sentiment extractor. More specifically, we provide a publicly available dataset containing fine-grained sentiment analysis data (minute-basis) about cryptocurrency market collected by different online sources. CryptoSentiment dataset includes 235,907 sentiment scores for 14 different cryptocurrencies gathered from various online sources such as news articles and social media.

This report delves into the correlation between Twitter engagement metrics, including likes, retweets, and influential tweets, and the price movements of the OCEAN token. By analyzing the relationship between these social media engagement indicators and the token's price, we aim to gain valuable insights into the impact of Twitter sentiment on OCEAN's market dynamics.

Additionally, this report showcases a Transformer model specifically designed for sentiment classification of tweets related to the OCEAN token. Leveraging the rich dataset of "The Twitter Financial Dataset (sentiment) version 1.0.0," the model classify tweets as bullish, bearish, or neutral. This classification capability allows us to gauge the prevailing sentiment of the Twitter community towards the OCEAN token.

The size and share of the market is categorized based on Application (Retail, Bfsi, Healthcare, Other) and Product (On-premises, Web-based) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Global Sentiment Analysis Software market size 2025 was XX Million. Sentiment Analysis Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

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Pricing Options:

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• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
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• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

Sentiment Analytics Software Market size was valued at USD 3.17 Billion in 2024 and is projected to reach USD 10.5 Billion by 2031, growing at a CAGR of 14.9% from 2024 to 2031.

Sentiment Analytics Software Market Drivers

Growth in Social Media Usage: As social media platforms are used more often for consumer engagement, communication, and brand promotion, there is a growing need for sentiment analytics software to track, examine, and extract insights from social media posts, comments, and feedback.

consumer Experience Management: In order to better understand consumer attitudes, preferences, and comments across a variety of channels, organizations place a high priority on customer experience management and sentiment analysis. This has led to the development of sentiment analytics software in an effort to increase customer happiness and loyalty.

Brand Reputation Management: The use of sentiment analytics software for brand monitoring, sentiment tracking, and reputation management is driven by the need to handle possible PR crises, maintain a positive brand sentiment, and monitor and manage brand reputation in real-time.