TDParse: Multi-target-specific Sentiment Recognition on Twitter

Bo Wang, Maria Liakata, Arkaitz Zubiaga, Rob Procter

EACL. 2017.

Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for single-target sentiment.

@inproceedings{wang2017tdparse,
  title={Tdparse: Multi-target-specific sentiment recognition on twitter},
  author={Wang, Bo and Liakata, Maria and Zubiaga, Arkaitz and Procter, Rob},
  booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  pages={483--493},
  year={2017}
}