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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Aspect Terms Extraction of Arabic Dialects for Opinion Mining Using Conditional Random Fields

verfasst von : Alawya Alawami

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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Abstract

While English opinion mining has been studied extensively, Arabic fine grained opinion mining has not received much attention. This paper looks at employing conditional random fields as a supervised method to extract aspect terms which can then be employed for fine grained opinion mining. Despite the lack of Arabic Dialect NLP tools that limited the amount of improvement that can be added to the algorithm, Our analysis shows a comparable level of precision and recall to what has been achieved for English.

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Vorheriges Kapitel Extracting Aspect Specific Sentiment Expressions Implying Negative Opinions
Nächstes Kapitel Large Scale Authorship Attribution of Online Reviews
Literatur
1.
Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. (TOIS) 26(3), 12 (2008)CrossRef
2.
Abdul-Mageed, M., Diab, M.T.: Awatif: a multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis. In: LREC, pp. 3907–3914 (2012)
3.
Abdul-Mageed, M., Korayem, M.: Automatic identification of subjectivity in morphologically rich languages: the case of Arabic. In: Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), Lisbon, pp. 2–6 (2010)
4.
Abdulla, N.A., Al-Ayyoub, M., Al-Kabi, M.N.: An extended analytical study of Arabic sentiments. Int. J. Big Data Intell. 1 1(1–2), 103–113 (2014)
5.
Ahmed, S., Pasquier, M., Qadah, G.: Key issues in conducting sentiment analysis on Arabic social media text. In: 2013 9th International Conference on Innovations in Information Technology (IIT), pp. 72–77. IEEE (2013)
6.
Al-Smadi, M., Qawasmeh, O., Talafha, B., Quwaider, M.: Human annotated Arabic dataset of book reviews for aspect based sentiment analysis. In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), pp. 726–730, August 2015
7.
Albraheem, L., Al-Khalifa, H.S.: Exploring the problems of sentiment analysis in informal Arabic. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, pp. 415–418. ACM (2012)
8.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
9.
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)
10.
Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 355–362. Association for Computational Linguistics (2005)
11.
Elhawary, M., Elfeky, M.: Mining Arabic business reviews. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1108–1113. IEEE (2010)
12.
Habash, N.Y.: Introduction to Arabic natural language processing. Synthesis Lect. Hum. Lang. Technol. 3(1), 1–187 (2010)CrossRef
13.
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)
14.
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
15.
Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045. Association for Computational Linguistics (2010)
16.
Jin, W., Ho, H.H., Srihari, R.K.: A novel lexicalized HMM-based learning framework for web opinion mining. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 465–472. Citeseer (2009)
17.
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)
18.
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009)
19.
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRef
20.
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web, pp. 342–351. ACM (2005)
21.
McCallum, A.K.: Mallet: a machine learning for language toolkit (2002)
22.
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)
23.
Omar, N., Albared, M., Al-Shabi, A.Q., Al-Moslmi, T.: Ensemble of classification algorithms for subjectivity and sentiment analysis of Arabic customers’ reviews. Int. J. Adv. Comput. Technol. 5(14), 77 (2013)
24.
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., Clercq, O.D., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jimnez-Zafra, S.M., Eryiit, G.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation. SemEval 2016. Association for Computational Linguistics, San Diego, California, June 2016
25.
Popescu, A.M., Nguyen, B., Etzioni, O.: Opine: extracting product features and opinions from reviews. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, pp. 32–33. Association for Computational Linguistics (2005)
26.
Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. ASSP Mag. IEEE 3(1), 4–16 (1986)CrossRef
27.
Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: Oca: opinion corpus for Arabic. J. Am. Soc. Inf. Sci. Technol. 62(10), 2045–2054 (2011)CrossRef
28.
Shoufan, A., Alameri, S.: Natural language processing for dialectical Arabic: a survey. In: Proceedings of the Second Workshop on Arabic Natural Language Processing, pp. 36–48. Association for Computational Linguistics, Beijing, China, July 2015. http://​www.​aclweb.​org/​anthology/​W15-3205
29.
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)
30.
Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics (2003)
31.
Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 43–50. ACM (2006)
Metadaten
Titel
Aspect Terms Extraction of Arabic Dialects for Opinion Mining Using Conditional Random Fields
verfasst von
Alawya Alawami
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75487-1_16

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