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:
Algorithm Selection Using Performance and Run Time Behavior Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Novel Method for Extracting Feature Opinion Pairs for Turkish

Authors : Hazal Türkmen, Ekin Ekinci, Sevinç İlhan Omurca

Published in: Artificial Intelligence: Methodology, Systems, and Applications

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Reviews made by online users, are one of the most important sources for consumers who give importance them during decision process and for companies which benefit from them during development process. Since internet has become a part of our daily lives, the number of reviews expands; it is getting difficult day by day to obtain a comprehensive view of user opinions from these reviews manually. Thus, sentiment analysis becomes an indispensable task for analyzing user reviews automatically. Recently, feature-based opinion mining methods are gaining importance in terms of fine-grained sentiment analysis. In this paper, we propose a Push Down Automata (PDA) based Feature-Opinion Pair (FOP) extraction for Turkish hotel reviews. At first, context free grammars are proposed by using Turkish linguistic relations then PDA is applied for extracting FOPs. Experimental results are showed that the proposed approach provides an efficient solution for discovering accurate FOPs.

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 Linking Tweets to News: Is All News of Interest?
next chapter In Search of Credible News
Literature
1.
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, California (2012)
2.
Picazo-Vela, S., Chou, S.Y., Melcher, A.J., Pearson, J.M.: Why provide an online review? An extended theory of planned behavior and the role of Big-Five personality traits. Comput. Hum. Behav. 26, 685–696 (2010)CrossRef
3.
Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63, 163–173 (2012)CrossRef
4.
Türkmen, H.: Ilhan Omurca S., Ekinci, E: An aspect based sentiment analysis on Turkish hotel reviews. Soc. GAU J. Appl. Sci. 6, 9–15 (2016)
5.
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: 19th National Conference on Artificial Intelligence, USA, pp. 755–760 (2004)
6.
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: International Conference on Knowledge Discovery and Data Mining, USA, pp. 168–177 (2004)
7.
Chan, K.T., King, I.: Let’s tango – finding the right couple for feature-opinion association in sentiment analysis. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 741–748. Springer, Heidelberg (2009)CrossRef
8.
Yin, P., Wang, H., Guo, K.: Feature–opinion pair identification of product reviews in Chinese: a domain ontology modeling method. New Rev. Hypermedia M. 19, 3–24 (2013)CrossRef
9.
Kamal, A.: Subjectivity classification using machine learning techniques for mining feature-opinion pairs from web opinion sources. IJCSI 10, 191–200 (2013)
10.
Zhou, E., Luo, X., Qin, Z.: Incorporating language patterns and domain knowledge into feature-opinion extraction. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2014. LNCS, vol. 8655, pp. 209–216. Springer, Heidelberg (2014)
11.
Quan, C., Ren, F.: Unsupervised product feature extraction for feature-oriented opinion determination. Inf. Sci. 272, 16–28 (2014)CrossRef
12.
Lakkaraju, H., Socher, R., Manning, C.: Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS Workshop on Deep Learning and Representation Learning, Canada, pp. 1–9 (2014)
13.
Fang, L., Liu, B., Huang, M.-L.: Leveraging large data with weak supervision for joint feature and opinion word extraction. J. Comput. Sci. Technol. 30, 903–916 (2015)CrossRef
14.
Che, W., Zhao, Y., Guo, H., Su, Z., Liu, T.: Sentence compression for aspect-based sentiment analysis. IEEE Audio Speech 23, 2111–2124 (2015)
15.
Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37, 9–27 (2011)CrossRef
16.
Eryiğit, G., Nivre, J., Oflazer, K.: Dependency parsing of Turkish. Comput. Linguist. 34, 357–389 (2008)CrossRef
17.
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Kao, A., Poteet, S.R. (eds.) Natural Language Processing and Text Mining, pp. 9–28. Springer, London (2007)CrossRef
18.
19.
Antunes, C., Oliveira, A.L.: Mining patterns using relaxations of user defined constraints. In: Proceedings of the Workshop on Knowledge Discovery in Inductive Databases (2004)
20.
Fokkink, W., Grune, D., Hond, B., Rutgers, P.: Detecting useless transitions in pushdown automata. arXiv preprint arXiv:​1306.​1947 (2013)
Metadata
Title
A Novel Method for Extracting Feature Opinion Pairs for Turkish
Authors
Hazal Türkmen
Ekin Ekinci
Sevinç İlhan Omurca
Copyright Year
2016
Book
Artificial Intelligence: Methodology, Systems, and Applications

Print ISBN: 978-3-319-44747-6

Electronic ISBN: 978-3-319-44748-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-44748-3_16

Premium Partner

    Image Credits