Open Access Open Access  Restricted Access Subscription Access

A TRANSFORMATION METHOD FOR ASPECT-BASED SENTIMENT ANALYSIS

Thin Van Dang, Vu Duc Nguyen, Nguyen Van Kiet, Nguyen Luu Thuy Ngan

Abstract


Along with the explosion of user reviews on the Internet, sentiment analysis has becomeone of the trending research topics in the field of natural language processing. In the last five years,many shared tasks were organized to keep track of the progress of sentiment analysis for various lan-guages. In the Fifth International Workshop on Vietnamese Language and Speech Processing (VLSP2018), the Sentiment Analysis shared task was the first evaluation campaign for the Vietnamese lan-guage. In this paper, we describe our system for this shared task. We employ a supervised learningmethod based on the Support Vector Machine classifiers combined with a variety of features. Weobtained the F1-score of 61% for both domains, which was ranked highest in the shared task. For theaspect detection subtask, our method achieved 77% and 69% in F1-score for the restaurant domainand the hotel domain respectively.

Keywords


sentiment analysis, aspect-based sentiment analysis, natural language processing, text analysis

Full Text:

PDF

References


P. D. Turney, “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, ser. ACL ’02, Philadelphia, Pennsylvania: Association for Computational Linguistics, 2002, pp. 417–424.

B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques”, in Proceedings of The 2002 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2002, pp. 79–86.

B. T. Kieu and S. B. Pham, “Sentiment Analysis for Vietnamese”, in 2010 Second International Conference on Knowledge and Systems Engineering (KSE), 2010, pp. 152–157.

T. Àlvarez-López, J. Juncal-Martínez, M. Fernández-Gavilanes, E. Costa-Montenegro, and F. J. González-Castano, “GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and ̃Unsupervised Aspect-Based Sentiment Analysis ”, in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), San Diego, California: Association for ComputationalLinguistics, 2016, pp. 306–311.

M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, “SemEval-2014 Task 4: Aspect Based Sentiment Analysis”, in Proceedings of The 8th International Workshop on Semantic Evaluation (SemEval 2014), Association for Computational Linguistics and Dublin City University, 2014, pp. 27–35.

Maria Pontiki and Dimitris Galanis and Haris Papageorgiou and Suresh Manandhar and Ion Androutsopoulos, “Semeval-2015 task 12: Aspect-based sentiment analysis”, in Proceedings of The 9th International Workshop on Semantic Evaluation (SemEval 2015), The Association for Computational Linguistics, 2015, pp. 486–495.

M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. AL-Smadi, M. Al-Ayyoub, Y. Zhao, B. Qin, O. D. Clercq, V. Hoste, M. Apidianaki, X.Tannier, N. Loukachevitch, E.Kotelnikov, N. Bel, S. M. Jiménez-Zafra, and G. Eryigit, “SemEval-2016 Task 5: Aspect Based Sentiment Analysis”, in Proceedings of The 10th International Workshop on Semantic Evaluation, ser. SemEval ’16, Association for Computational Linguistics, 2016.

B. Liu, “Sentiment Analysis and Opinion Mining”, Synthesis Lectures on Human Language Technologies, vol. 5, pp. 1–167, 2012.

T. Chen, R. Xu, Y. He, and X. Wang, “Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN”, Expert System Applications, vol. 72, pp. 221–230, 2017.

M. Hu and B. Liu, “Mining Opinion Features in Customer Reviews”, in Proceedings of Nineteenth National Conference on Artificial Intelligence (AAAI-2004), 2004.

G. Ganu, N. Elhadad, and A. Marian, “Beyond the Stars: Improving Rating Predictions using Review Text Content”, in 12th International Workshop on the Web and Databases, 2009.

T. T. Thet, J.-C. Na, and C. S. Khoo, “Aspect-based Sentiment Analysis of Movie Reviews on Discussion Boards”, Journal of Information Science, vol. 36, no. 6, pp. 823–848, 2010.

Y. Jo and A. H. Oh, “Aspect and sentiment unification model for online review analysis”, in Proceedings of The Fourth ACM International Conference on Web Search and Data Mining, ser. WSDM ’11, ACM, 2011, pp. 815–824.

I. Perikos, K. Kovas, F. Grivokostopoulou, and I.Hatzilygeroudis, “A System for Aspect-based Opinion Mining of Hotel Reviews”, in Proceedings of The 13th International Conference on Web Information Systems and Technologies, WEBIST, 2017, pp. 388–394.

K. Schouten and F. Frasincar, “Survey on Aspect-Level Sentiment Analysis”, IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 3, pp. 813–830, 2016.

D.-H. Phan and T.-D. Cao, “Applying Skip-gram Word Estimation and SVM-based Classification for Opinion Mining Vietnamese Food Places Text Reviews”, in Proceedings of The Fifth Symposium on Information and Communication Technology, ser. SoICT ’14, 2014, pp. 232–239.

N. T. Duyen, N. X. Bach, and T. M. Phuong, “An empirical study on sentiment analysis for Vietnamese”, in 2014 International Conference on Advanced Technologies for Communications (ATC 2014), 2014, pp. 309–314.

H. S. Le, T. V. Le, and T. V. Pham, “Aspect Analysis for Opinion Mining of Vietnamese Text”, in 2015 International Conference on Advanced Computing and Applications (ACOMP), 2015, pp. 118–123.

T. Bang, C. Haruechaiyasak, and V. Sornlertlamvanich, “Vietnamese sentiment analysis based on term feature selection approach”, in Proceedings of The 10th International Conference on Knowledge Information and Creativity Support Systems (KICSS 2015), 2015, pp. 196–204.

N. X. Bach, P. D. Van, N. D. Tai, and T. M. Phuong, “Mining Vietnamese Comparative Sentences for Sentiment Analysis”, in 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), 2015, pp. 162–167.

S. Trinh, L. Nguyen, M. Vo, and P. Do, “Lexicon-Based Sentiment Analysis of Facebook Comments in Vietnamese Language”, in Recent Developments in Intelligent Information and Database Systems, D. Król, L. Madeyski, and N. T. Nguyen, Eds. Cham: Springer International Publishing, 2016, pp. 263–276.

G. Tsoumakas and I. Katakis, “Multi-Label Classification: An Overview”, International Journal of Data Warehousing and Mining (IJDWM), vol. 3, no. 3, pp. 1–13, 2007.




DOI: https://doi.org/10.15625/1813-9663/34/4/13162

Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology