Thsi repository contains dataset used in Detecting and Explaining Causes From Text For a Time Series Event, EMNLP'17. Please contact Dongyeop Kang (dongyeok@cs.cmu.edu) if you have any questions.
How-to-download
./download_extract.sh
This script will automatically download all datasets and extract each zipped file into separate directories.
Dataset
The format is [Date] \t [Count/Probability]:
- sentis: sentiment (positive/negatie) time series for each company and politician
- topics: topic time series for each company and politician
- topics.sentis: sentiment of each topic time series for each company and politician
- unigram: uni-gram time series (12,804 words), uni.filtered.events contains temporal dynamics of each word
- bigram: bi-gram time series (25,909 words), uni.filtered.events contains temporal dynamics of each word
For better replication, we additionaly share following data:
- Stock prices used in the experiment are udpated under ./stock_price.
- 10K tweet IDs per each day are also shared under ./tweet_ids_10k_per_day.
Reference
If you think this dataset is useful for your research, please consider citing this paper.
@inproceedings{kang2017detecting,
title={Detecting and Explaining Causes From Text For a Time Series Event},
author={Kang, Dongyeop and Gangal, Varun and Lu, Ang and Chen, Zheng and Hovy, Eduard},
booktitle={Conference on Empirical Methods on Natural Language Processing},
year={2017}
}
License
MIT