Skip to main content
Log in

Aspect Level Sentiment Analysis Based on Deep Learning and Ontologies

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Aspect level sentiment analysis has received much attention by researchers over the last few years. It aims first to determine the aspects in a given text (e.g., a comment, a sentence, a review, etc.) and second to perform the sentiment analysis (i.e., determine the polarity, such as positive, negative, or neutral) of the corresponding text with respect to each aspect. In this paper, we propose an original method of sentiment analysis for Tunisian social media. Our method is mainly based on domain ontologies for aspect extraction and deep learning models for aspect sentiment classification. Evaluation results are very encouraging, since we outperformed the baseline method with an enhancement of 17% for the task of aspect level sentiment classification.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

All data will be soon accessible to the community via the following link: https://sites.google.com/site/anlprg/corpora-corpus.

References

  1. Rahman H, Tariq J, Masood MA, Subahi AF, Khalaf OI, Alotaibi Y. Multi-tier sentiment analysis of social media text using supervised machine learning. Comput Mater Contin. 2023;74:5527–43.

    Google Scholar 

  2. Tun Thura Thet, Na J-C, Khoo CSG. Aspect-based sentiment analysis of movie reviews on discussion boards. J Inf Sci. 2010;36:823–48. https://doi.org/10.1177/0165551510388123.

    Article  Google Scholar 

  3. Matin Pour AA, Jalili S. Aspects extraction for aspect level opinion analysis based on deep CNN. In: 2021 26th International Computer Conference, Computer Society of Iran (CSICC). 2021; p. 1–6. IEEE, Tehran, Iran.

  4. Samha KA, Li Y, Zhang J. Aspect-based opinion extraction from customer reviews. In: Computer Science & Information Technology ( CS & IT ). 2014; pp. 149–160. Academy & Industry Research Collaboration Center (AIRCC).

  5. Mowlaei ME, Saniee Abadeh M, Keshavarz H. Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Syst Appl. 2020;148: 113234.

    Article  Google Scholar 

  6. Chen J, Wang R, Fang B, Zuo C. Fine-grained aspect-based opinion mining on online course reviews for feedback analysis. Interact Learn Environ. 2023;33:1–16.

    Google Scholar 

  7. Tamrakar S, Bal BK, Thapa RB. Aspect based sentiment analysis of Nepali text using support vector machine and Naive Bayes. Tech J. 2020;2:22–9.

    Article  Google Scholar 

  8. Mubarok MS, Adiwijaya Aldhi MD. Aspect-based sentiment analysis to review products using Naïve Bayes. In: Presented at the International Conference on mathematics: pure, applied and computation: empowering engineering using mathematics, Surabaya, Indonesia (2017).

  9. Khamphakdee N, Seresangtakul P. An efficient deep learning for Thai sentiment analysis. Data. 2023;8:90. https://doi.org/10.3390/data8050090.

    Article  Google Scholar 

  10. Al-Dabet S, Tedmori S, Al-Smadi M. Extracting opinion targets using attention-based neural model. SN Comput Sci. 2020;1:242.

    Article  Google Scholar 

  11. Abdelgwad MM, Soliman THA, Taloba AI, Farghaly MF. Arabic aspect based sentiment analysis using bidirectional GRU based models. J King Saud Univ Comput Inform Sci. 2021;34(9):6652–62.

    Google Scholar 

  12. Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic re-views. Int J Mach Learn Cybern. 2019;10:2163–75.

    Article  Google Scholar 

  13. Mai L, Le B. Joint sentence and aspect-level sentiment analysis of product comments. Ann Oper Res. 2021;300:493–513.

    Article  MathSciNet  MATH  Google Scholar 

  14. Belguith M, Aloulou C, Gargouri B. Building domain ontologies for Tunisian dialect: towards aspect sentiment analysis from social media. In: ISPR’ 2023, Hammamet, Tunisia.

  15. Masmoudi A, Hamdi J, Hadrich Belguith L. Deep learning for sentiment analysis of Tunisian dialect. CyS. 2021. https://doi.org/10.13053/cys-25-1-3472.

    Article  Google Scholar 

  16. Mekki A, Zribi I, Ellouze M, Belguith HL. Treebank creation and parser generation for Tunisian social media text. In: 2020 IEEE/ACS 17th International Conference on computer systems and applications (AICCSA). 2020; pp. 1–8. IEEE, Antalya, Turkey. https://doi.org/10.1109/AICCSA50499.2020.9316462.

  17. Schouten K, Frasincar F, de Jong F. Ontology-enhanced aspect-based sentiment analysis. In: Cabot J, De Virgilio R, Torlone R, editors. Web engineering. Cham: Springer International Publishing; 2017. p. 302–20.

    Chapter  Google Scholar 

  18. Thakor P, Sasi S. Ontology-based sentiment analysis process for social media content. Proc Comput Sci. 2015;53:199–207.

    Article  Google Scholar 

  19. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.

    Article  Google Scholar 

  20. ArunKumar KE, Kalaga DV, Mohan Sai Kumar CH, Kawaji M, Brenza TM. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alex Eng J. 2022;61:7585–603.

    Article  Google Scholar 

  21. Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. 2014. arXiv:1406.1078 [cs, stat].

  22. Belguith M, Azaiez N, Aloulou C, Gargouri B. Social Media sentiment classification for Tunisian Dialect: a deep learning approach. ISPR’ 2022, Hammamet, Tunisia.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehdi Belguith.

Ethics declarations

Conflict of Interest 

All authors declare that they have no conflict of interest.

Research Involving Human Participants and/or Animals

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Our Dataset is composed of comments in Tunisian dialect. These comments are collected from social media (Facebook and YouTube). All sensitive and personalized content was removed for users’ privacy concerns.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Belguith, M., Aloulou, C. & Gargouri, B. Aspect Level Sentiment Analysis Based on Deep Learning and Ontologies. SN COMPUT. SCI. 5, 58 (2024). https://doi.org/10.1007/s42979-023-02362-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02362-3

Keywords

Navigation