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
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 8/2019

01-03-2018 | Original Article

Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews

Authors: Mohammad Al-Smadi, Bashar Talafha, Mahmoud Al-Ayyoub, Yaser Jararweh

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2019

Log in

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

search-config
loading …

Abstract

This paper proposes a state-of-the-art research for aspect-based sentiment analysis of Arabic Hotels’ reviews using two implementations of long short-term memory (LSTM) neural networks. The first one is (a) a character-level bidirectional LSTM along with conditional random field classifier (Bi-LSTM-CRF) for aspect opinion target expressions (OTEs) extraction, and the second one is (b) an aspect-based LSTM for aspect sentiment polarity classification in which the aspect-OTEs are considered as attention expressions to support the sentiment polarity identification. Proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Results show that our approaches outperform baseline research on both tasks with an enhancement of 39% for the task of aspect-OTEs extraction and 6% for the aspect sentiment polarity classification task.
previous article Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop
next article Sentiment analysis in teaching evaluations using sentiment phrase pattern matching (SPPM) based on association mining

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
Show more products
Footnotes
Literature
1.
Abdul-Mageed M, Diab M (2012) Toward building a large-scale Arabic sentiment lexicon. In: Proceedings of the 6th international global WordNet conference, pp 18–22
2.
Agerri R, Bermudez J, Rigau G (2014) Ixa pipeline: efficient and ready to use multilingual nlp tools. In: LREC, pp 3823–3828
3.
Al-Smadi M, Al-Ayyoub M, Al-Sarhan H, Jararweh Y (2015a) Using aspect-based sentiment analysis to evaluate arabic news affect on readers. In: 2015 IEEE/ACM 8th international conference on utility and cloud computing (UCC), IEEE, pp 436–441
4.
Al-Smadi M, Qawasmeh O, Talafha B, Quwaider M (2015b) Human annotated Arabic dataset of book reviews for aspect based sentiment analysis. In: future internet of things and cloud (FiCloud), 2015 3rd international conference, IEEE, pp 726–730
5.
Al-Smadi M, Al-Ayyoub M, Al-Sarhan H, Jararweh Y (2016a) An aspect-based sentiment analysis approach to evaluating Arabic news affect on readers. J Univers Comput Sci (JUCS) 22:630–649MathSciNet
6.
Al-Smadi M, Obaidat I, Al-Ayyoub M, Mohawesh R, Jararweh Y (2016b) Using enhanced lexicon-based approaches for the determination of aspect categories and their polarities in Arabic reviews. Int J Inf Technol Web Eng (IJITWE) 11(3):15–31CrossRef
7.
Al-Smadi M, Qawasmeh O, Talafha B, Al-Ayyoub M, Jararweh Y, Benkhalifa E (2016c) An enhanced framework for aspect-based sentiment analysis of hotels reviews: Arabic reviews case study. In: International conference for internet technology and secured transactions (ICITST-2016), IEEE, pp 98–103
8.
Al-Twairesh N, Al-Khalifa H, Al-Salman A (2014) Subjectivity and sentiment analysis of Arabic: trends and challenges. In: 2014 IEEE/ACS 11th international conference on computer systems and applications (AICCSA), IEEE, pp 148–155
9.
Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. LREC 10:2200–2204
10.
Badaro G, Baly R, Hajj H, Habash N, El-Hajj W (2014) A large scale arabic sentiment lexicon for Arabic opinion mining. ANLP 2014:165
11.
Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow I, Bergeron A, Bouchard N, Warde-Farley D, Bengio Y (2012) Theano: new features and speed improvements. arXiv:​1211.​5590
12.
13.
Brown PF, Desouza PV, Mercer RL, Pietra VJD, Lai JC (1992) Class-based n-gram models of natural language. Comput Linguist 18(4):467–479
14.
Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15–21CrossRef
15.
Castellucci G, Filice S, Croce D, Basili R (2014) Unitor: aspect based sentiment analysis with structured learning. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), Citeseer, pp 761–767
16.
Chernyshevich M (2014) Ihs r&d belarus: cross-domain extraction of product features using conditional random fields. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 309–313
17.
Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354CrossRef
18.
Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder–decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Association for computational linguistics, Doha, pp 1724–1734. http://​www.​aclweb.​org/​anthology/​D14-1179
19.
Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:​1412.​3555
20.
Chung J, Cho K, Bengio Y (2016) A character-level decoder without explicit segmentation for neural machine translation. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers), Association for computational linguistics, Berlin, pp 1693–1703. http://​www.​aclweb.​org/​anthology/​P16-1160
21.
Clark A (2003) Combining distributional and morphological information for part of speech induction. In: Proceedings of the 10th conference on European chapter of the Association for Computational Linguistics-Volume 1, Association for Computational Linguistics, pp 59–66
22.
Collins M (2002) Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing-Volume 10, Association for Computational Linguistics, pp 1–8
23.
Dahou A, Xiong S, Zhou J, Haddoud MH, Duan P (2016) Word embeddings and convolutional neural network for Arabic sentiment classification. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, The COLING 2016 Organizing Committee, Osaka, pp 2418–2427. http://​aclweb.​org/​anthology/​C16-1228
24.
Dean J, Corrado G, Monga R, Chen K, Devin M, Mao M, Senior A, Tucker P, Yang K, Le QV et al (2012) Large scale distributed deep networks. In: Advances in neural information processing systems, pp 1223–1231
25.
26.
Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 513–520
27.
Goller C, Kuchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. In: Neural networks, 1996, IEEE international conference, IEEE, vol 1, pp 347–352
28.
29.
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw 18(5):602–610CrossRef
30.
Gridach M (2016) Character-aware neural networks for Arabic named entity recognition for social media. In: Proceedings of the 6th workshop on South and Southeast Asian natural language processing (WSSANLP2016), The COLING 2016 Organizing Committee, Osaka, pp 23–32. http://​aclweb.​org/​anthology/​W16-3703
31.
Habash NY (2010) Introduction to arabic natural language processing. Synth Lect Hum Lang Technol 3(1):1–187CrossRef
32.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef
33.
Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 168–177
34.
Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Association for computational linguistics, Doha, pp 720–728. http://​www.​aclweb.​org/​anthology/​D14-1080
35.
36.
Kiritchenko S, Zhu X, Cherry C, Mohammad S (2014a) Nrc-canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 437–442
37.
Kiritchenko S, Zhu X, Mohammad SM (2014b) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762CrossRef
38.
Kumar A, Kohail S, Kumar A, Ekbal A, Biemann C (2016) Iit-tuda at semeval-2016 task 5: beyond sentiment lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), Association for computational linguistics, San Diego, pp 1129–1135. http://​www.​aclweb.​org/​anthology/​S16-1174
39.
Lafferty J, McCallum A, Pereira F et al (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, ICML, vol 1, pp 282–289
40.
Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, Association for computational linguistics, San Diego, pp 260–270. http://​www.​aclweb.​org/​anthology/​N16-1030
41.
Li X, Xie H, Chen L, Wang J, Deng X (2014) News impact on stock price return via sentiment analysis. Knowl Based Syst 69:14–23CrossRef
42.
43.
Ling W, Dyer C, Black AW, Trancoso I, Fermandez R, Amir S, Marujo L, Luis T (2015) Finding function in form: Compositional character models for open vocabulary word representation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, Association for computational linguistics, Lisbon, pp 1520–1530. http://​aclweb.​org/​anthology/​D15-1176
44.
Liou CY, Huang JC, Yang WC (2008) Modeling word perception using the elman network. Neurocomputing 71(16):3150–3157CrossRef
45.
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167CrossRef
46.
Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-Volume 1, Association for computational linguistics, pp 142–150
47.
Mahyoub FH, Siddiqui MA, Dahab MY (2014) Building an Arabic sentiment lexicon using semi-supervised learning. J King Saud Univ Comput Inf Sci 26(4):417–424
48.
49.
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013a) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119
50.
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119
51.
Mohammad SM, Turney PD (2010) Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, Association for computational linguistics, pp 26–34
52.
Mohammad SM, Kiritchenko S, Zhu X (2013) Nrc-canada: building the state-of-the-art in sentiment analysis of tweets. arXiv:​1308.​6242
53.
Nguyen TH, Shirai K (2015) Phrasernn: phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the 2015 conference on empirical methods in natural language processing, Association for computational linguistics, Lisbon, pp 2509–2514. https://​aclweb.​org/​anthology/​D/​D15/​D15-1298
54.
55.
Obaidat I, Mohawesh R, Al-Ayyoub M, Al-Smadi M, Jararweh Y (2015) Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches. In: Applied electrical engineering and computing technologies (AEECT), 2015 IEEE Jordan Conference, IEEE, pp 1–6
56.
Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), Citeseer, pp 27–35
57.
Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I (2015) Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), Association for computational linguistics, Denver, pp 486–495
58.
Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O, Hoste V, Apidianaki M, Tannier X, Loukachevitch N, Kotelnikov E, Bel N, Jiménez-Zafra SM, Eryiğit G (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), Association for computational linguistics, pp 19–30. http://​www.​aclweb.​org/​anthology/​S16-1002
59.
Popescu AM, Etzioni O (2007) Extracting product features and opinions from reviews. In: Natural language processing and text mining. Springer, London, pp 9–28
60.
Rao Y, Xie H, Li J, Jin F, Wang FL, Li Q (2016) Social emotion classification of short text via topic-level maximum entropy model. Inf Manag 53(8):978–986CrossRef
61.
Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46CrossRef
62.
Rosenthal S, Ritter A, Nakov P, Stoyanov V (2014) Semeval-2014 task 9: sentiment analysis in twitter. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 73–80
63.
Ruder S, Ghaffari P, Breslin JG (2016) Insight-1 at semeval-2016 task 5: deep learning for multilingual aspect-based sentiment analysis. arXiv:​160902748
64.
Saias J (2015) Sentiue: target and aspect based sentiment analysis in semeval-2015 task 12. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Association for Computational Linguistics, pp 767–771
65.
Saif M, Mohammad MS, Kiritchenko S (2016) Sentiment lexicons for Arabic social media. In: Proceedings of 10th edition of the the language resources and evaluation conference (LREC), Portorož
66.
Salameh M, Mohammad S, Kiritchenko S (2015) Sentiment after translation: a case-study on Arabic social media posts. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, Association for computational linguistics, Denver, pp 767–777. http://​www.​aclweb.​org/​anthology/​N15-1078
67.
San Vicente I, Saralegi X, Agerri R, Sebastián DS (2015) Elixa: a modular and flexible absa platform. In: SemEval-2015, p 748
68.
dos Santos CN, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, pp 69–78
69.
Scaffidi C, Bierhoff K, Chang E, Felker M, Ng H, Jin C (2007) Red opal: product-feature scoring from reviews. In: Proceedings of the 8th ACM conference on electronic commerce. ACM, pp 182–191
70.
Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C et al (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), Citeseer, vol 1631, p 1642
71.
Tamchyna A, Veselovská K (2016) Ufal at semeval-2016 task 5: recurrent neural networks for sentence classification. Proceedings of SemEval, pp 367–371
72.
Tang D, Wei F, Qin B, Liu T, Zhou M (2014) Coooolll: a deep learning system for twitter sentiment classification. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 208–212
73.
Tang D, Qin B, Feng X, Liu T (2016) Effective lstms for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, The COLING 2016 Organizing Committee, Osaka, pp 3298–3307. http://​aclweb.​org/​anthology/​C16-1311
74.
Todhunter J, Sovpel I, Pastanohau D (2013) System and method for automatic semantic labeling of natural language texts. US Patent 8,583,422
75.
Toh Z, Su J (2015) Nlangp: supervised machine learning system for aspect category classification and opinion target extraction, Association for Computational Linguistics, pp 496–501
76.
Toh Z, Wang W (2014) Dlirec: aspect term extraction and term polarity classification system. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 235–240
77.
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(Dec):3371–3408MathSciNetMATH
78.
Vinodhini G, Chandrasekaran R (2016) A sampling based sentiment mining approach for e-commerce applications. Inf Process Manag 53(1):223–236CrossRef
79.
Wagner J, Arora P, Cortes S, Barman U, Bogdanova D, Foster J, Tounsi L (2014) Dcu: aspect-based polarity classification for semeval task 4. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Association for Computational Linguistics and Dublin City University, pp 223–229
80.
Wang Y, Huang M, zhu x, Zhao L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, Association for computational linguistics, Austin, pp 606–615. https://​aclweb.​org/​anthology/​D16-1058
81.
Wiebe J, Wilson T, Cardie C (2005) Annotating expressions of opinions and emotions in language. Lang Resour Eval 39(2–3):165–210CrossRef
82.
Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y, Cardie C, Riloff E, Patwardhan S (2005) Opinionfinder: a system for subjectivity analysis. In: Proceedings of hlt/emnlp on interactive demonstrations, Association for Computational Linguistics, pp 34–35
83.
Yoo KH, Gretzel U (2008) What motivates consumers to write online travel reviews? Inf Technol Tour 10(4):283–295CrossRef
84.
Zhang Z, Lan M (2015) Ecnu: extracting effective features from multiple sequential sentences for target-dependent sentiment analysis in reviews. In: SemEval-2015, p 736
Metadata
Title
Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews
Authors
Mohammad Al-Smadi
Bashar Talafha
Mahmoud Al-Ayyoub
Yaser Jararweh
Publication date
01-03-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0799-4

Other articles of this Issue 8/2019

International Journal of Machine Learning and Cybernetics 8/2019 Go to the issue

Original Article

Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry

Editorial

Editorial: Affective and sentimental computing

Original Article

Polychronicity tendency-based online behavioral signature

Original Article

Leveraging semantics for sentiment polarity detection in social media

Original Article

FineNews: fine-grained semantic sentiment analysis on financial microblogs and news

Original Article

On relational learning and discovery in social networks: a survey