Mining Tweets that refer to TV programs with Deep Neural Networks
Takeshi Kobayakawa | Taro Miyazaki | Hiroki Okamoto | Simon Clippingdale
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
The automatic analysis of expressions of opinion has been well studied in the opinion mining area, but a remaining problem is robustness for user-generated texts. Although consumer-generated texts are valuable since they contain a great number and wide variety of user evaluations, spelling inconsistency and the variety of expressions make analysis difficult. In order to tackle such situations, we applied a model that is reported to handle context in many natural language processing areas, to the problem of extracting references to the opinion target from text. Experiments on tweets that refer to television programs show that the model can extract such references with more than 90% accuracy.
Latent Dynamic Model with Category Transition Constraint for Opinion Classification
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers
Syntax-Driven Sentence Revision for Broadcast News Summarization
Hideki Tanaka | Akinori Kinoshita | Takeshi Kobayakawa | Tadashi Kumano | Naoto Katoh
Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+Sum 2009)
- Taro Miyazaki 1
- Hiroki Okamoto 1
- Simon Clippingdale 1
- Hideki Tanaka 1
- Akinori Kinoshita 1
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