SentiStream is a co-training framework for adaptive online sentiment analysis over continuously evolving data streams. This DataSys repository preserves the paper artifact together with the earlier PLStream labeling pipeline used by the project.
SentiStream/contains the SentiStream implementation, datasets, experiment scripts, and detailed setup instructions.PLStream/contains the earlier Apache Flink and Redis polarity labeling pipeline.
Yuhao Wu, Karthick Sharma, Chun Wei Seah, and Shuhao Zhang. "SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams." Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6198-6212, 2023.
- Paper: https://doi.org/10.18653/v1/2023.emnlp-main.380
- Canonical repository: https://github.com/DataSysResearch/SentiStream
@inproceedings{wu-etal-2023-sentistream,
title = {{SentiStream}: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams},
author = {Wu, Yuhao and Sharma, Karthick and Seah, Chun Wei and Zhang, Shuhao},
booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
year = {2023},
pages = {6198--6212},
doi = {10.18653/v1/2023.emnlp-main.380},
url = {https://aclanthology.org/2023.emnlp-main.380}
}This is a research artifact maintained for reproducibility. Start with the component README that matches the experiment you intend to run.