dykang/cgraph
dataset for Detecting and Explaining Causes From Text For a Time Series Event, EMNLP'17
This project provides a dataset for understanding how text-based information influences real-world time-series events. It takes various text data like sentiment, topics, and n-gram frequencies, alongside stock prices and tweet IDs, to help you explore causal relationships. A data scientist or researcher in social science or finance would use this to analyze event causation.
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Use this if you are a researcher or data scientist looking for a comprehensive dataset to study how textual content, such as news or social media, impacts time-series events like stock price fluctuations.
Not ideal if you are looking for a tool or an algorithm to perform causal inference directly, as this provides only the raw data.
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Aug 31, 2020
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