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2019 | OriginalPaper | Chapter

Dependency-Aware Attention Model for Emotion Analysis for Online News

Authors : Xue Zhao, Ying Zhang, Xiaojie Yuan

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

This paper studies the emotion responses evoked by the news articles. Most work focuses on extracting effective features from text for emotion classification. As a result, the valuable information contained in the emotion labels has been largely neglected. In addition, all words are potentially conveying affective meaning yet they are not equally significant. Traditional attention mechanism can be leveraged to extract important words according to the word-label co-occurrence pattern. However, words that are important to the less popular emotions are still difficult to identify. Because emotions have intrinsic correlations, by integrating such correlations into attention mechanism, emotion triggering words can be detected more accurately. In this paper, we come up with an emotion dependency-aware attention model, which makes the best use of label information and the emotion dependency prior knowledge. The experiments on two public news datasets have proved the effectiveness of the proposed model.

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Footnotes
Literature
1.
Ben-Ze’ev, A.: The Subtlety of Emotions. MIT Press, Cambridge (2001)
2.
Rao, Y., Lei, J., Wenyin, L., Li, Q., Chen, M.: Building emotional dictionary for sentiment analysis of online news. WWW 17, 723–742 (2014)CrossRef
3.
Staiano, J., Guerini, M.: Depeche Mood: a lexicon for emotion analysis from crowd annotated news. In: ACL (2014)
4.
Bao, S., et al.: Mining social emotions from affective text. TKDE 24, 1658–1670 (2012)
5.
Vaswani, A., et al.: Attention is all you need. In: NIPS (2017)
6.
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: EMNLP (2015)
7.
Gao, S., Ramanathan, A., Tourassi, G.: Hierarchical convolutional attention networks for text classification. In: Proceedings of The Third Workshop on Representation Learning for NLP (2018)
8.
Wang, G., et al.: Joint embedding of words and labels for text classification. In: ACL (2018)
9.
Ortony, A., Turner, T.J.: What’s basic about basic emotions? Psychol. Rev. 97, 315–331 (1990)CrossRef
10.
11.
Strapparava, C., Mihalcea, R.: Semeval-2007 task 14: affective text, June 2007
12.
Zhou, D., Yang, Y., He, Y.: Relevant emotion ranking from text constrained with emotion relationships. In: NAACL (2018)
13.
Deyu, Z.H.O.U., Zhang, X., Zhou, Y., Zhao, Q., Geng, X.: Emotion distribution learning from texts. In: EMNLP (2016)
14.
Quan, X., Wang, Q., Zhang, Y., Si, L., Wenyin, L.: Latent discriminative models for social emotion detection with emotional dependency. TOIS 34, 2:1–2:19 (2015)CrossRef
15.
Song, K., Gao, W., Chen, L., Feng, S., Wang, D., Zhang, C.: Build emotion lexicon from the mood of crowd via topic-assisted joint non-negative matrix factorization. In: SIGIR (2016)
16.
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP (2015)
17.
Lin, Z., et al.: A structured self-attentive sentence embedding. In: ICLR (2017)
18.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: NAACL (2016)
19.
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP (2014)
20.
Szegedy, C., et al.: Going deeper with convolutions. In: ICLR (2015)
21.
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (2017)
22.
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: EMNLP (2014)
23.
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29, 436–465 (2013)MathSciNetCrossRef
24.
Mohammad, S.: From once upon a time to happily ever after: tracking emotions in novels and fairy tales. In: ACL (2011)
25.
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC (2010)
26.
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRef
27.
Strapparava, C., Valitutti, A.: WordNet affect: an affective extension of wordnet. In: LREC, vol. 4, pp. 1083–1086, May 2004
28.
Li, X., et al.: Weighted multi-label classification model for sentiment analysis of online news. In: BigComp (2016)
29.
Bao, S., et al.: Joint emotion-topic modeling for social affective text mining. In: ICDM (2009)
Metadata
Title
Dependency-Aware Attention Model for Emotion Analysis for Online News
Authors
Xue Zhao
Ying Zhang
Xiaojie Yuan
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16147-7

Electronic ISBN: 978-3-030-16148-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16148-4_14

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