Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Supporting Tool for IT System Security Specification Evaluation Based on ISO/IEC 15408 and ISO/IEC 18045 Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Sentiment Analysis Based on LSTM Architecture with Emoticon Attention

verfasst von : Changliang Li, Changsong Li, Pengyuan Liu

Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Sentiment analysis is one of the most important research directions in natural language processing field. People increasingly use emoticons in text to express their sentiment. However, most existing algorithms for sentiment classification only focus on text information but don’t full make use of the emoticon information. To address this issue, we propose a novel LSTM architecture with emoticon attention to incorporate emoticon information into sentiment analysis. Emoticon attention is employed to use emoticons to capture crucial semantic components. To evaluate the efficiency of our model, we build the first sentiment corpus with rich emoticons from movie review website and we use it as our experiment dataset. Experiments results show that our approach is able to better use emoticon information to improve the performance on sentiment analysis.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Overall Loss for Deep Neural Networks
Nächstes Kapitel Aspect Level Sentiment Analysis with Aspect Attention
Literatur
1.
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Empirical Methods in Natural Language Processing, pp. 79–86 (2002)
2.
Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:​1308.​6242 (2013)
3.
Socher, R., Bauer, J., Manning, C.D.: Parsing with compositional vector grammars. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 455–465 (2013)
4.
Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Empirical Methods in Natural Language Processing, pp. 1201–1211 (2012)
5.
Socher, R., Lin, C.C., Manning, C.D., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, pp. 129–136 (2011)
6.
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP (2014)
7.
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224 N Project Report, Stanford, vol. 1 (2009)
8.
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc (2010)
9.
Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., Kaymak, U.: Exploiting emoticons in sentiment analysis. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 703–710 (2013)
10.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)
11.
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:​1509.​00685 (2015)
12.
Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., et al.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, pp. 1693–1701 (2015)
13.
Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)CrossRef
14.
McGlohon, M., Glance, N.S., Reiter, Z.: Star quality: aggregating reviews to rank products and merchants. In: ICWSM (2010)
15.
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: ICWSM, vol. 10, pp. 178–185 (2010)
16.
Yano, T., Smith, N.A.: What’s worthy of comment? Content and comment volume in political blogs. In: ICWSM (2010)
17.
Sadikov, E., Parameswaran, A.G., Venetis, P.: Blogs as predictors of movie success. In: ICWSM (2009)
18.
Miller, M., Sathi, C., Wiesenthal, D., Leskovec, J., Potts, C.: Sentiment flow through hyperlink networks. In: ICWSM (2011)
19.
Feldman, R., Rosenfeld, B., Bar-Haim, R., Fresko, M.: The stock sonar—sentiment analysis of stocks based on a hybrid approach. In: Twenty-Third IAAI Conference (2011)
20.
Groh, G., Hauffa, J.: Characterizing social relations via NLP-based sentiment analysis. In: ICWSM (2011)
21.
22.
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)
23.
Socher, R., Pennington, J., Huang, E., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Empirical Methods in Natural Language Processing, pp. 151–161 (2011)
24.
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)
25.
Santos, C.N.D., Gatti, M.A.D.C.: Deep convolutional neural networks for sentiment analysis of short texts. In: International Conference on Computational Linguistics, pp. 69–78 (2014)
26.
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: ACL (2015)
27.
Kendon, A.: On gesture: its complementary relationship with speech. In: Nonverbal Behavior and Communication, pp. 65–97 (1987)
28.
Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 675–682 (2009)
29.
Jurafsky, D.: Speech & Language Processing. Pearson Education India, Noida (2000)
30.
Tang, D., Qin, B., Feng, X., Liu, T.: Target-dependent sentiment classification with long short term memory. CoRR, abs/1512.01100 (2015)
31.
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention
32.
Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)
33.
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:​1404.​2188 (2014)
Metadaten
Titel
Sentiment Analysis Based on LSTM Architecture with Emoticon Attention
verfasst von
Changliang Li
Changsong Li
Pengyuan Liu
Copyright-Jahr
2019
Buch
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-26141-2

Electronic ISBN: 978-3-030-26142-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-26142-9_21