International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 10, Issue 10 (October 2023), Pages: 94-102

----------------------------------------------

 Original Research Paper

Efficient social media sentiment analysis using confidence interval-based classification of online product brands

 Author(s): 

 Tawfik Guesmi 1, *, Fawaz Al-Janfawi 1, Ramzi Guesmi 2, Mansoor Alturki 1

 Affiliation(s):

 1Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il, Saudi Arabia
 2LETI-Laboratory, National Engineering School of Sfax, University of Sfax, Soukra Sfax, Tunisia

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-8221-2610

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2023.10.011

 Abstract

This paper presents an efficient method for categorizing the sentiments of Internet users, with a focus on social media users, using a confidence interval to estimate the reliability of sentiment predictions. The classification is based on the sentiments expressed in their posts, which are divided into positive, negative, and neutral categories. The paper presents an analysis table that analyzes sentiments and opinions about online product brands. The process includes two steps: 1) analyzing sentiments from text data using machine learning techniques, and 2) describing a five-step sentiment and opinion classification process that includes data collection, preprocessing, algorithm application, validation, and visualization. The proposed solution is implemented using Python, along with the scikit-learn, NumPy, pandas, and Dash libraries, and leverages the use of confidence intervals to assess the accuracy and reliability of the sentiment analysis model.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Machine learning, SVM, NLP, Sentiment analysis, Confidence intervals

 Article history

 Received 15 April 2023, Received in revised form 16 September 2023, Accepted 21 September 2023

 Acknowledgment 

This research has been funded by the Scientific Research Deanship at the University of Ha’il–Saudi Arabia through project number GR-22 089.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Guesmi T, Al-Janfawi F, Guesmi R, and Alturki M (2023). Efficient social media sentiment analysis using confidence interval-based classification of online product brands. International Journal of Advanced and Applied Sciences, 10(10): 94-102

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 

 Tables

 Table 1 Table 2 Table 3

----------------------------------------------   

 References (33)

  1. Alsaif H, Guesmi R, Alshammari BM, Hamrouni T, Guesmi T, Alzamil A, and Belguesmi L (2022). A novel data augmentation-based brain tumor detection using convolutional neural network. Applied Sciences, 12(8): 3773. https://doi.org/10.3390/app12083773   [Google Scholar]
  2. Basiri ME and Kabiri A (2017). Translation is not enough: Comparing lexicon-based methods for sentiment analysis in Persian. In the 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE), IEEE, Shiraz, Iran: 36-41. https://doi.org/10.1109/CSICSSE.2017.8320114   [Google Scholar]
  3. Basiri ME, Ghasem-Aghaee N, and Reza A (2017). Lexicon-based sentiment analysis in Persian. Current and Future Developments in Artificial Intelligence, 30: 154-183. https://doi.org/10.2174/9781681085029117010009   [Google Scholar]
  4. Basiri ME, Nemati S, Abdar M, Cambria E, and Acharya UR (2021). ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems, 115: 279-294. https://doi.org/10.1016/j.future.2020.08.005   [Google Scholar]
  5. Boukes M, Van de Velde B, Araujo T, and Vliegenthart R (2020). What’s the tone? Easy doesn’t do it: Analyzing performance and agreement between off-the-shelf sentiment analysis tools. Communication Methods and Measures, 14(2): 83-104. https://doi.org/10.1080/19312458.2019.1671966   [Google Scholar]
  6. Cambria E, Poria S, Hazarika D, and Kwok K (2018). SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, USA, 32(1). https://doi.org/10.1609/aaai.v32i1.11559   [Google Scholar]
  7. Chikersal P, Poria S, Cambria E, Gelbukh A, and Siong CE (2015). Modelling public sentiment in Twitter: Using linguistic patterns to enhance supervised learning. Computational Linguistics and Intelligent Text Processing: 16th International Conference, Springer International Publishing, Cairo, Egypt: 49-65. https://doi.org/10.1007/978-3-319-18117-2_4   [Google Scholar]
  8. Chu E and Roy D (2017). Audio-visual sentiment analysis for learning emotional arcs in movies. In the 2017 IEEE International Conference on Data Mining (ICDM), IEEE, New Orleans, USA: 829-834. https://doi.org/10.1109/ICDM.2017.100   [Google Scholar] PMCid:PMC5412413
  9. Friedman N, Geiger D, and Goldszmidt M (1997). Bayesian network classifiers. Machine Learning, 29: 131-163. https://doi.org/10.1023/A:1007465528199   [Google Scholar]
  10. Ghiassi M and Lee S (2018). A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Systems with Applications, 106: 197-216. https://doi.org/10.1016/j.eswa.2018.04.006   [Google Scholar]
  11. Hastie T, Tibshirani R, and Friedman J (2009). Boosting and additive trees. In: Hastie T, Tibshirani R, and Friedman J (Eds.), The elements of statistical learning: Data mining, inference, and prediction: 337-387. Springer, New York, USA. https://doi.org/10.1007/978-0-387-84858-7_10   [Google Scholar]
  12. Hatzivassiloglou V and McKeown K (1997). Predicting the semantic orientation of adjectives. In the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of The European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Madrid, Spain: 174-181. https://doi.org/10.3115/976909.979640   [Google Scholar]
  13. Hochreiter S and Schmidhuber J (1997). Long short-term memory. Neural computation, 9(8): 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735   [Google Scholar] PMid:9377276
  14. John GH and Langley P (2013). Estimating continuous distributions in Bayesian classifiers. ArXiv Preprint ArXiv: 1302.4964. https://doi.org/10.48550/arXiv.1302.4964   [Google Scholar]
  15. Kaur BR, Navneet K, Ravneet K, and Gurpreet K (2016). Opinion mining and sentiment analysis. In the 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, New Delhi, India: 452-455.   [Google Scholar]
  16. Kharde V and Sonawane P (2016). Sentiment analysis of Twitter data: A survey of techniques. Arxiv Preprint Arxiv: 1601.06971. https://doi.org/10.5120/ijca2016908625   [Google Scholar]
  17. Lenc L and Hercig T (2016). Neural networks for sentiment analysis in Czech. ITAT 2016 Proceedings, CEUR Workshop Proceedings, 1649: 48-55.   [Google Scholar]
  18. Liu B (2017). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge, UK.   [Google Scholar]
  19. Liu B (2022). Sentiment analysis and opinion mining. Springer Nature, Berlin, Germany.   [Google Scholar]
  20. Mahyoob M, Al-Garaady J, Alblwi A, and Alrahaili M (2022). Sentiment analysis of public tweets towards the emergence of SARS-CoV-2 Omicron variant: A social media analytics framework. Engineering, Technology and Applied Science Research, 12(3): 8525–8531. https://doi.org/10.48084/etasr.4865   [Google Scholar]
  21. Mudinas A, Zhang D, and Levene M (2012). Combining lexicon and learning based approaches for concept-level sentiment analysis. Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, Association for Computing Machinery, Beijing, China: 152-165. https://doi.org/10.1145/2346676.2346681   [Google Scholar]
  22. Pang B, Lee L, and Vaithyanathan S (2002). Thumbs up? Sentiment classification using machine learning techniques. Arxiv Preprint cs/0205070. https://doi.org/10.3115/1118693.1118704   [Google Scholar]
  23. Pisner DA and Schnyer DM (2020). Support vector machine. Academic Press, Cambridge, USA. https://doi.org/10.1016/B978-0-12-815739-8.00006-7   [Google Scholar]
  24. Pradhan VM, Vala J, and Balani P (2016). A survey on sentiment analysis algorithms for opinion mining. International Journal of Computer Applications, 133(9): 7-11. https://doi.org/10.5120/ijca2016907977   [Google Scholar]
  25. Priyam A, Abhijeeta GR, Rathee A, and Srivastava S (2013). Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology, 3(2): 334-337.   [Google Scholar]
  26. Sánchez-Núñez P, Cobo MJ, De las Heras-Pedrosa C, Pelaez JI, and Herrera-Viedma E (2020). Opinion mining, sentiment analysis and emotion understanding in advertising: A bibliometric analysis. IEEE Access, 8: 134563-134576. https://doi.org/10.1109/ACCESS.2020.3009482   [Google Scholar]
  27. Sandoval-Almazan R and Valle-Cruz D (2018). Facebook impact and sentiment analysis on political campaigns. Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, Association for Computing Machinery, Delft, Netherlands: 1-7. https://doi.org/10.1145/3209281.3209328   [Google Scholar]
  28. Šperková L (2018). Review of latent Dirichlet allocation methods usable in voice of customer analysis. Acta Informatica Pragensia, 7(2): 152-165. https://doi.org/10.18267/j.aip.120   [Google Scholar]
  29. Täckström O and McDonald R (2011). Semi-supervised latent variable models for sentence-level sentiment analysis. In the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Portland, USA: 569-574.   [Google Scholar]
  30. Witten IH, Frank E, and Hall MA (2005). Data minding: Practical machine learning tools and techniques. 3rd Edition, Morgan Kaufmann Publishers, San Francisco, USA.   [Google Scholar]
  31. Wong M and Farooq B (2020). A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data. Transportation Research Part C: Emerging Technologies, 110: 247-268. https://doi.org/10.1016/j.trc.2019.11.022   [Google Scholar]
  32. Zhang L, Ghosh R, Dekhil M, Hsu M, and Liu B (2011). Combining lexicon-based and learning-based methods for Twitter sentiment analysis. HP Laboratories, Technical Report HPL-2011, 89: 1-8.   [Google Scholar]
  33. Zhao J, Liu K, and Xu L (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge, UK.   [Google Scholar]