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Massive Stock Sentiment Dataset
Finance & Banking Analytics
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About
This dataset provides a substantial collection of news sentences paired with their corresponding sentiment, primarily intended for financial analysis and stock prediction. With over 100,000 rows, each entry indicates whether the news is positive (represented by '1') or negative/neutral (represented by '0'), offering insights into potential stock movement. A positive sentiment suggests a likely increase in stock value, while a negative or neutral sentiment indicates a likely decrease [1, 2]. It is noted that the data within this dataset is not shuffled [2].
Columns
- Sentiment: A numerical label indicating the sentiment of the news sentence. A value of 0 denotes negative or neutral sentiment, suggesting a stock price might go down. A value of 1 denotes positive sentiment, suggesting a stock price might go up [1, 2]. There are 53,026 instances of 0 and 55,725 instances of 1, making a total of 108,301 unique values in this column [3].
- Sentence: The actual text of the news article sentence [1, 2]. This column contains the textual data analysed for sentiment.
Distribution
The dataset typically comes in CSV format [4] and consists of over 100,000 rows of data [2]. It includes two primary columns: 'Sentiment' and 'Sentence' [1]. The data is presented in an unshuffled order [2]. Specific numbers for records are available for each sentiment label: 53,026 rows for sentiment '0' and 55,725 rows for sentiment '1' [3].
Usage
This dataset is ideal for news sentiment analysis and stock prediction [1]. It can be employed to train machine learning models to forecast stock market movements based on news sentiment [1, 2]. Other use cases include developing financial analytics tools, performing large-scale text analysis on financial news, and researching the correlation between media sentiment and economic indicators [2].
Coverage
The dataset's regional scope is global [5]. The time range of the data is not specified in the provided information. No specific demographic scope is mentioned for the news sources or the subjects of the news.
License
CC-BY-NC
Who Can Use It
This dataset is particularly useful for:
- Data Scientists and Machine Learning Engineers: For building and training Natural Language Processing (NLP) models to analyse sentiment in text and predict financial outcomes [2].
- Financial Analysts and Researchers: To gain insights into how news sentiment impacts stock performance and for market forecasting [1].
- Developers: To integrate sentiment analysis capabilities into financial applications or trading algorithms.
- Academics: For research into financial economics, sentiment analysis, and predictive analytics.
Dataset Name Suggestions
- Stock News Sentiment for Market Prediction
- Financial News Sentiment Analysis Dataset
- Massive Stock Sentiment Data
- Market News Sentiment for Stock Forecasting
Attributes
Original Data Source: Stock News Sentiment Analysis(Massive Dataset)
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