Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model

GUO, Siwen; Höhn, Sviatlana; Xu, Feiyu; SCHOMMER, Christoph

doi:10.1007/978-3-030-05453-3_10

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Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model

GUO, Siwen; Höhn, Sviatlana; Xu, Feiyu et al.

2019 • In Agents and Artificial Intelligence

Peer reviewed

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https://hdl.handle.net/10993/38467

DOI
10.1007/978-3-030-05453-3_10

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Keywords :

Sentiment Analysis; Hawkes Process; Personalized Model; Attention Network; Recurrent Neural Networks

Abstract :

[en] People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person’s past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research.

Disciplines :

Computer science

Author, co-author :

GUO, Siwen ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

Höhn, Sviatlana ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

Xu, Feiyu; Lenovo > AI Lab, Beijing, China

SCHOMMER, Christoph ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

External co-authors :

yes

Language :

English

Title :

Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model

Publication date :

2019

Event name :

International Conference on Agents and Artificial Intelligence 2018

Event date :

January 2018

Audience :

International

Main work title :

Agents and Artificial Intelligence

Publisher :

Springer

ISBN/EAN :

978-3-030-05453-3

Collection name :

Lecture Notes in Computer Science, vol 11352.

Peer reviewed :

Peer reviewed

Additional URL :

https://link.springer.com/chapter/10.1007/978-3-030-05453-3_10

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since 21 January 2019

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Bibliography

Meena, A., Prabhakar, T.V.: Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 573–580. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71496-5 53
Wiebe, J., Wilson, T., Bell, M.: Identifying collocations for recognizing opinions. In: Proceedings of the ACL Workshop on Collocation: Computational Extraction, Analysis, and Exploitation, pp. 24–31 (2001)
Cheng, X., Xu, F.: Fine-grained opinion topic and polarity identification. In: LREC, pp. 2710–2714 (2008)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of SemEval, pp. 27–35 (2014)
Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments and Emotions. Cambridge University Press, Cambridge (2015)
Reiter, E., Sripada, S.: Human variation and lexical choice. Comput. Linguist. 28, 545–553 (2002)
Markovikj, D., Gievska, S., Kosinski, M., Stillwell, D.: Mining Facebook data for predictive personality modeling. In: Proceedings of the 7th international AAAI conference on Weblogsand Social Media (ICWSM 2013), Boston, pp. 23–26 (2013)
Guo, S., Höhn, S., Xu, F., Schommer, C.: PERSEUS: a personalization framework for sentiment categorization with recurrent neural network. In: International Conference on Agents and Artificial Intelligence, p. 9, 16–18 January 2018, Funchal (2018)
Hawkes, A.G.: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58, 83–90 (1971)
Ogata, Y.: Space-time point-process models for earthquake occurrences. Ann. Inst. Stat. Math. 50, 379–402 (1998)
Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. J. Am. Stat. Assoc. 106, 100–108 (2011)
Bacry, E., Mastromatteo, I., Muzy, J.F.: Hawkes processes in finance. Mark. Microstruct. Liq. 1, 1550005 (2015)
Kobayashi, R., Lambiotte, R.: TiDeH: Time-dependent Hawkes process for predicting retweet dynamics. In: ICWSM, pp. 191–200 (2016)
Zhao, Q., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J.:Seismic: A self-exciting point process model for predicting tweetpopularity. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1513–1522. ACM (2015)
Gilbert, C.H.E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International Conference on Weblogs and Social Media (ICWSM 2014) (2014). http://comp.social.gatech.edu/papers/icwsm14. vader.hutto.pdf. Accessed 20 Apr 2016
Saif, H., He, Y., Fernandez, M., Alani, H.: Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manag. 52, 5–19 (2016)
Gong, L., Al Boni, M., Wang, H.: Modeling social norms evolution for personalized sentiment classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, (Volume 1: Long Papers), vol. 1, pp. 855–865 (2016)
Gong, L., Haines, B., Wang, H.: Clustered model adaption for personalized sentiment analysis. In: Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 937–946 (2017)
Song, K., Feng, S., Gao, W., Wang, D., Yu, G., Wong, K.F.: Personalized sentiment classification based on latent individuality of microblog users. In: IJCA, pp. 2277– 2283 (2015)
Chen, T., Xu, R., He, Y., Xia, Y., Wang, X.: Learning user and product distributed representations using a sequence model for sentiment analysis. IEEE Comput. Intell. Mag. 11, 34–44 (2016)
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1650–1659 (2016)
Dou, Z.Y.: Capturing user and product information for document level sentiment analysis with deep memory network. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 521–526 (2017)
Tang, D., Qin, B., Liu, T.: Learning semantic representations of users and products for document level sentiment classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, (Volume 1: Long Papers), pp. 1014–1023 (2015)
Guo, S., Schommer, C.: Embedding of the personalized sentiment engine PERSEUS in an artificial companion. In: International Conference on Companion Technology, Ulm, 11–13 September 2017. IEEE (2017)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 ieeeinternational conference on Acoustics, speech and signal processing (ICASSP), pp. 6645–6649. IEEE (2013)
Johnson, M., et al.: Google’s multilingual neural machine translation system: enabling zero-shot translation. arXiv preprint arXiv:1611.04558 (2016)
Cambria, E., Poria, S., Bajpai, R., Schuller, B.W.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING, pp. 2666–2677 (2016)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Jia, L., Yu, C., Meng, W.: The effect of negation on sentiment analysis and retrieval effectiveness. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1827–1830. ACM (2009)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 6000–6010 (2017)
Zaremba, W., Sutskever, I.: Learning to execute. arXiv preprint arXiv:1410.4615 (2014)
Laub, P.J., Taimre, T., Pollett, P.K.: Hawkes processes. arXiv preprint arXiv:1507.02822 (2015)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Surowiecki, J.: The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business. Econ. Soc. Nat. 296 (2004)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1 (2009)
Schommer, C., Kampas, D., Bersan, R.: A prospect on how to find the polarity of a financial news by keeping an objective standpoint. In: Proceedings ICAART 2013 (2013)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Cao, Q., Shen, H., Cen, K., Ouyang, W., Cheng, X.: DeepHawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1149–1158. ACM (2017)