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        <title>Mining Tweets that refer to TV programs with Deep Neural Networks</title>
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        <namePart type="given">Takeshi</namePart>
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        <namePart type="given">Taro</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Mining Tweets that refer to TV programs with Deep Neural Networks
%A Kobayakawa, Takeshi
%A Miyazaki, Taro
%A Okamoto, Hiroki
%A Clippingdale, Simon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F kobayakawa-etal-2019-mining
%X 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.
%R 10.18653/v1/D19-5517
%U https://aclanthology.org/D19-5517
%U https://doi.org/10.18653/v1/D19-5517
%P 126-130

Markdown (Informal)

[Mining Tweets that refer to TV programs with Deep Neural Networks](https://aclanthology.org/D19-5517) (Kobayakawa et al., WNUT 2019)

• Mining Tweets that refer to TV programs with Deep Neural Networks (Kobayakawa et al., WNUT 2019)

ACL

• Takeshi Kobayakawa, Taro Miyazaki, Hiroki Okamoto, and Simon Clippingdale. 2019. Mining Tweets that refer to TV programs with Deep Neural Networks. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 126–130, Hong Kong, China. Association for Computational Linguistics.