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2018 | OriginalPaper | Buchkapitel

Construction of a Multi-dimensional Vectorized Affective Lexicon

verfasst von : Yang Wang, Chong Feng, Qian Liu

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Affective analysis has received growing attention from both research community and industry. However, previous works either cannot express the complex and compound states of human’s feelings or rely heavily on manual intervention. In this paper, by adopting Plutchik’s wheel of emotions, we propose a lowcost construction method that utilizes word embeddings and high-quality small seed-sets of affective words to generate multi-dimensional affective vector automatically. And a large-scale affective lexicon is constructed as a verification, which could map each word to a vector in the affective space. Meanwhile, the construction procedure uses little supervision or manual intervention, and could learn affective knowledge from huge amount of raw corpus automatically. Experimental results on affective classification task and contextual polarity disambiguation task demonstrate that the proposed affective lexicon outperforms other state-of-the-art affective lexicons.

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Literatur
1.
Zurück zum Zitat Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC 2010, vol. 10, pp. 2200–2204 (2010) Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC 2010, vol. 10, pp. 2200–2204 (2010)
3.
Zurück zum Zitat Cambria, E., Poria, S., Bajpai, R., Schuller, B.W.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING 2016, pp. 2666–2677 (2016) Cambria, E., Poria, S., Bajpai, R., Schuller, B.W.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING 2016, pp. 2666–2677 (2016)
4.
Zurück zum Zitat Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, vol. 10 (2010) Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, vol. 10 (2010)
5.
Zurück zum Zitat Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI 2010. vol. 5, p. 3 (2010) Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI 2010. vol. 5, p. 3 (2010)
6.
Zurück zum Zitat Dong, Z., Dong, Q., Hao, C.: HowNet and its computation of meaning. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 53–56 (2010) Dong, Z., Dong, Q., Hao, C.: HowNet and its computation of meaning. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 53–56 (2010)
7.
Zurück zum Zitat Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17, 1–26 (2007) Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17, 1–26 (2007)
8.
Zurück zum Zitat Fellbaum, C.: WordNet. Wiley Online Library, New York (1998)MATH Fellbaum, C.: WordNet. Wiley Online Library, New York (1998)MATH
9.
Zurück zum Zitat Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, vol. 1, no. 12 (2009) Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, vol. 1, no. 12 (2009)
11.
Zurück zum Zitat Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI, pp. 100–107 (2006) Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI, pp. 100–107 (2006)
12.
Zurück zum Zitat Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 142–150 (2011) Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 142–150 (2011)
13.
Zurück zum Zitat Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv (2013) Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv (2013)
14.
Zurück zum Zitat Nozza, D., Fersini, E., Messina, E.: A multi-view sentiment corpus. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, vol. 1, pp. 273–280 (2017) Nozza, D., Fersini, E., Messina, E.: A multi-view sentiment corpus. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, vol. 1, pp. 273–280 (2017)
15.
Zurück zum Zitat Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10, pp. 79–86. Association for Computational Linguistics (2002) Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10, pp. 79–86. Association for Computational Linguistics (2002)
16.
Zurück zum Zitat Plutchik, R.: The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am. Sci. 89(4), 344–350 (2001)CrossRef Plutchik, R.: The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am. Sci. 89(4), 344–350 (2001)CrossRef
17.
Zurück zum Zitat Ribeiro, F.N., Araújo, M., Gonçalves, P., Gonçalves, M.A., Benevenuto, F.: SentiBench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5(1), 1–29 (2016)CrossRef Ribeiro, F.N., Araújo, M., Gonçalves, P., Gonçalves, M.A., Benevenuto, F.: SentiBench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5(1), 1–29 (2016)CrossRef
18.
Zurück zum Zitat Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. TKDE 28(3), 813–830 (2016) Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. TKDE 28(3), 813–830 (2016)
19.
Zurück zum Zitat Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I.: Finki at SemEval-2016 task 4: deep learning architecture for Ttwitter sentiment analysis. In: SemEval 2016, pp. 149–154 (2016) Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I.: Finki at SemEval-2016 task 4: deep learning architecture for Ttwitter sentiment analysis. In: SemEval 2016, pp. 149–154 (2016)
20.
Zurück zum Zitat Strapparava, C., Valitutti, A., et al.: WordNet affect: an affective extension of WordNet. In: LREC, vol. 4, pp. 1083–1086 (2004) Strapparava, C., Valitutti, A., et al.: WordNet affect: an affective extension of WordNet. In: LREC, vol. 4, pp. 1083–1086 (2004)
22.
Zurück zum Zitat Tang, D., Wei, F., Qin, B., Yang, N., Liu, T., Zhou, M.: Sentiment embeddings with applications to sentiment analysis. TKDE 28(2), 496–509 (2016) Tang, D., Wei, F., Qin, B., Yang, N., Liu, T., Zhou, M.: Sentiment embeddings with applications to sentiment analysis. TKDE 28(2), 496–509 (2016)
23.
Zurück zum Zitat Thelwall, M.: Heart and soul: sentiment strength detection in the social web with sentistrength. In:Cyberemotions: Collective emotions in cyberspace (2013) Thelwall, M.: Heart and soul: sentiment strength detection in the social web with sentistrength. In:Cyberemotions: Collective emotions in cyberspace (2013)
Metadaten
Titel
Construction of a Multi-dimensional Vectorized Affective Lexicon
verfasst von
Yang Wang
Chong Feng
Qian Liu
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-99501-4_28

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