Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees Read chapter Read first chapter

2018 | OriginalPaper | Chapter

WS4ABSA: An NMF-Based Weakly-Supervised Approach for Aspect-Based Sentiment Analysis with Application to Online Reviews

Authors : Alberto Purpura, Chiara Masiero, Gian Antonio Susto

Published in: Discovery Science

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The goal of Aspect-Based Sentiment Analysis is to identify opinions regarding specific targets and the corresponding sentiment polarity in a document. The proposed approach is designed for real-world scenarios, where the amount of available information and annotated data is often too limited to train supervised models. We focus on the two core tasks of Aspect-Based Sentiment Analysis: aspect and sentiment polarity classification. The first task – which consists in the identification of the opinion targets in a document – is tackled by means of a weakly-supervised technique based on Non-negative Matrix Factorization. This strategy allows users to easily embed some a priori domain knowledge by means of short seed terms lists. Experimental results on publicly available data sets related to online reviews suggest that the proposed approach is very flexible and can be easily adapted to different languages and domains.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Most Important First – Keyphrase Scoring for Improved Ranking in Settings With Limited Keyphrases
next chapter Finding Topic-Specific Trends and Influential Users in Social Networks
Footnotes
1
In this work we employed the stopwords and stemmers provided in Python nltk 3.2.5, https://​www.​nltk.​org.
 
2
In this work we will consider corpora in English and Spanish (see Sect. 4); the lists of negation terms for English (16 terms) and Spanish (12 terms) are included in our code repository https://​gitlab.​dei.​unipd.​it/​dl_​dei/​ws4absa.
 
3
The code for deploying and evaluating WS4ABSA  is available on https://​gitlab.​dei.​unipd.​it/​dl_​dei/​ws4absa.
 
5
The difference was computed as: \(\text {difference} \div \text {other value} * 100\).
 
Literature
1.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal Mach. Learn. Res. 3, 993–1022 (2003)MATH
2.
Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812. Association for Computational Linguistics (2010)
3.
Choo, J., Lee, C., Reddy, C.K., Park, H.: Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Vis. Comput. Graph. 19(12), 1992–2001 (2013)CrossRef
4.
Cichocki, A., Phan, A.H.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans. Fundam. Electron., Commun. Comput. Sci. 92(3), 708–721 (2009)CrossRef
5.
García-Pablos, A., Cuadros, M., Rigau, G.: W2vlda: almost unsupervised system for aspect based sentiment analysis. Expert. Syst. Appl. 91, 127–137 (2018)CrossRef
6.
Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713–730 (2008)MathSciNetCrossRef
7.
Kim, J., He, Y., Park, H.: Algorithms for nonnegative matrix and tensor factorizations: A unified view based on block coordinate descent framework. J. Glob. Optim. 58(2), 285–319 (2014)MathSciNetCrossRef
8.
9.
Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems, vol. 15. SIAM, Philadelphia (1995)CrossRef
10.
Li, T., Sindhwani, V., Ding, C., Zhang, Y.: Bridging domains with words: Opinion analysis with matrix tri-factorizations. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 293–302. SIAM (2010)
11.
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRef
12.
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-vol. 1, pp. 142–150. Association for Computational Linguistics (2011)
13.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
14.
Paatero, P., Tapper, U.: Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (1994)CrossRef
15.
Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19–30 (2016)
16.
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015)
17.
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)CrossRef
18.
Toh, Z., Su, J.: Nlangp at semeval-2016 task 5: improving aspect based sentiment analysis using neural network features. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 282–288 (2016)
19.
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2006)CrossRef
20.
Varghese, R., Jayasree, M.: Aspect based sentiment analysis using support vector machine classifier. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1581–1586. IEEE (2013)
21.
22.
Wang, F., Li, T., Zhang, C.: Semi-supervised clustering via matrix factorization. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 1–12. SIAM (2008)
23.
Xiang, B., Zhou, L.: Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 434–439 (2014)
24.
Yan, X., Guo, J., Liu, S., Cheng, X., Wang, Y.: Learning topics in short texts by non-negative matrix factorization on term correlation matrix. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 749–757. SIAM (2013)CrossRef
25.
Zagibalov, T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of chinese text. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pp. 1073–1080. Association for Computational Linguistics (2008)
26.
Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a maxent-lda hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56–65. Association for Computational Linguistics (2010)
Metadata
Title
WS4ABSA: An NMF-Based Weakly-Supervised Approach for Aspect-Based Sentiment Analysis with Application to Online Reviews
Authors
Alberto Purpura
Chiara Masiero
Gian Antonio Susto
Copyright Year
2018
Book
Discovery Science

Print ISBN: 978-3-030-01770-5

Electronic ISBN: 978-3-030-01771-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01771-2_25

Premium Partner

    Image Credits