Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computing and Information Technology

IC2IT 2017: Recent Advances in Information and Communication Technology 2017 pp 317–326Cite as

Improving Aspect Extraction Using Aspect Frequency and Semantic Similarity-Based Approach for Aspect-Based Sentiment Analysis

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 566))

Abstract

Identifying the targets of users’ opinions, referred as aspects, in aspect-based sentiment analysis, is the most important and crucial task. A large number of approaches have been proposed to accomplish this task. These approaches identify a huge amount of potential aspects from customer reviews. But not all the extracted aspects are interesting and include terms which are not related to the product and these irrelevant terms affect the performance of the aspect extraction approaches. Therefore, in this paper, we are proposing a two-level aspect pruning approach to eliminate irrelevant aspects. The proposed approach performs the task of aspect pruning in two steps: (a) by calculating the frequency of each word and selecting the most frequent aspects; and (b) by calculating the semantic similarity of non-frequent words and eliminate aspects which are not semantically related to the product. Our experimental evaluation has shown a significant improvement of the proposed approach over the compared approaches.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.nltk.org/.

  2. 2.

    https://code.google.com/p/word2vec/.

  3. 3.

    https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html.

References

  1. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)

    Article  Google Scholar 

  2. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008)

    Article  Google Scholar 

  3. Rana, T.A., Cheah, Y.-N.: Aspect extraction in sentiment analysis: comparative analysis and aurvey. Artif. Intell. Rev. 46, 459–483 (2016)

    Article  Google Scholar 

  4. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: 10th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 168–177. ACM (2004)

    Google Scholar 

  5. Rana, T.A., Cheah, Y.-N., Letchmunan, S.: Topic modeling in sentiment analysis: a systematic review. J. ICT Res. Appl. 10, 76–93 (2016)

    Article  Google Scholar 

  6. Bafna, K., Toshniwal, D.: Feature based summarization of customers’ reviews of online products. Proc. Comput. Sci. 22, 142–151 (2013)

    Article  Google Scholar 

  7. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: 19th National Conference on Artificial intelligence, pp. 755–760. San Jose (2004)

    Google Scholar 

  8. Kang, Y., Zhou, L.: RubE: rule-based methods for extracting product features from online consumer reviews. Inf. Manag. 54, 166–176 (2016)

    Article  Google Scholar 

  9. Liu, Q., Liu, B., Zhang, Y., Kim, D.S., Gao, Z.: Improving opinion aspect extraction using semantic similarity and aspect associations. In: 13th AAAI Conference on Artificial Intelligence (AAAI), Phoenix (2016)

    Google Scholar 

  10. Popescu, A.-M., Etzioni, O.: Extracting Product Features and Opinions from Reviews. Natural Language Processing and Text Mining, pp. 9–28. Springer, New York (2007)

    Book  Google Scholar 

  11. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: 4th International Conference on Knowledge Discovery and Data Mining (KDD) (1998)

    Google Scholar 

  12. Rana, T.A., Cheah, Y.-N.: Sequential patterns-based rules for aspect-based sentiment analysis. In: 3rd International Conference on Computational Science and Technology (ICCST) (2016)

    Google Scholar 

  13. Eirinaki, M., Pisal, S., Singh, J.: Feature-based opinion mining and ranking. J. Comput. Syst. Sci. 78, 1175–1184 (2012)

    Article  MathSciNet  Google Scholar 

  14. Bagheri, A., Saraee, M., de Jong, F.: An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews. Natural Language Processing and Information Systems, pp. 140–151. Springer (2013)

    Google Scholar 

  15. Du, J., Chan, W., Zhou, X.: A Product aspects identification method by using translation-based language model. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 2790–2795. IEEE (2014)

    Google Scholar 

  16. Hai, Z., Chang, K., Kim, J.-J., Yang, C.C.: Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans. Knowl. Data Eng. 26, 623–634 (2014)

    Article  Google Scholar 

  17. Yu, J., Zha, Z.-J., Wang, M., Chua, T.-S.: Aspect ranking: identifying important product aspects from online consumer reviews. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1496–1505. ACL (2011)

    Google Scholar 

  18. Ma, B., Zhang, D., Yan, Z., Kim, T.: An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. J. Electron. Commerce Res. 14, 304–314 (2013)

    Google Scholar 

  19. Liu, K., Xu, L., Zhao, J.: Opinion target extraction using word-based translation model. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1346–1356. ACL (2012)

    Google Scholar 

  20. Xu, L., Liu, K., Lai, S., Chen, Y., Zhao, J.: Mining opinion words and opinion targets in a two-stage framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1764–1773 (2013)

    Google Scholar 

  21. Rana, T.A., Cheah, Y.-N.: Exploiting sequential patterns to detect objective aspects from online reviews. In: 3rd International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA), pp. 1–5. IEEE (2016)

    Google Scholar 

  22. Cruz, F.L., Troyano, J.A., Enríquez, F., Ortega, F.J., Vallejo, C.G.: Long autonomy or long delay? The importance of domain in opinion mining. Expert Syst. Appl. 40, 3174–3184 (2013)

    Article  Google Scholar 

  23. Liu, Q., Gao, Z., Liu, B., Zhang, Y.: Automated rule selection for opinion target extraction. Knowl. Based Syst. 104, 74–88 (2016)

    Article  Google Scholar 

  24. Poria, S., Cambria, E., Ku, L.-W., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: 2nd Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 28–37. 28 (2014)

    Google Scholar 

  25. Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37, 9–27 (2011)

    Article  Google Scholar 

  26. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Conference on Empirical Methods in Natural Language Processing, pp. 1533–1541. ACL (2009)

    Google Scholar 

  27. Yu, J., Zha, Z.-J., Wang, M., Wang, K., Chua, T.-S.: Domain-assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews. In: Conference on Empirical Methods in Natural Language Processing, pp. 140–150. ACL (2011)

    Google Scholar 

  28. Zhang, L., Liu, B., Lim, S.H., O’Brien-Strain, E.: Extracting and ranking product features in opinion documents. In: 23rd International Conference on Computational Linguistics, Posters, pp. 1462–1470. ACL (2010)

    Google Scholar 

  29. Cilibrasi, R.L., Vitanyi, P.M.: The Google similarity distance. IEEE Trans. Knowl. Data Eng. 19, 370–383 (2007)

    Article  Google Scholar 

  30. Rana, T.A., Cheah, Y.-N.: Hybrid rule-based approach for aspect extraction and categorization from customer reviews. In: 9th International Conference on IT in Asia (CITA), pp. 1–5. IEEE (2015)

    Google Scholar 

  31. Miller, G., Fellbaum, C.: Wordnet: An Electronic Lexical Database. MIT Press, Boston (1998)

    MATH  Google Scholar 

Download references

Acknowledgements

Toqir A. Rana would like to gratefully acknowledge the Ministry of Higher Education (MOHE), Malaysia, for supporting his studies under the Malaysian International Scholarship (MIS) program.

Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

    Toqir A. Rana & Yu-N Cheah

Authors
  1. Toqir A. Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-N Cheah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toqir A. Rana .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Rana, T.A., Cheah, YN. (2018). Improving Aspect Extraction Using Aspect Frequency and Semantic Similarity-Based Approach for Aspect-Based Sentiment Analysis. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60663-7_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60662-0

  • Online ISBN: 978-3-319-60663-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation