DOI QR코드

DOI QR Code

Frequency Matrix Based Summaries of Negative and Positive Reviews

  • Almuhannad Sulaiman Alorfi (Department of Information System, Faculty of Computing and Information Technology in Rabigh (FCITR) King Abdul Aziz University (KAU) )
  • Received : 2023.03.05
  • Accepted : 2023.03.20
  • Published : 2023.03.30

Abstract

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

Keywords

References

  1. N. Nedjah, I. Santos, and L. de Macedo Mourelle, "Sentiment analysis using convolutional neural network via word embeddings," Evol. Intell., vol. 15, no. 4, pp. 2295-2319, 2022, doi: 10.1007/s12065-019-00227-4. 
  2. M. Iram, S. U. Rehman, S. Shahid, and S. A. Mehmood, "Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach," in Proceedings of the Pakistan Academy of Sciences, 2022, vol. 59, no. January, pp. 63-75. https://doi.org/10.53560/PPASA(59-2)771
  3. B. Agarwal, N. Mittal, P. Bansal, and S. Garg, "Sentiment analysis using common-sense and context information," Comput. Intell. Neurosci., vol. 2015, 2015, doi: 10.1155/2015/715730. 
  4. N. Alami, M. Meknassi, S. Alaoui Ouatik, and N. Ennahnahi, "Arabic text summarization based on graph theory," in International Journal of Computer Applications, 2017, vol. 64, p. 5, doi: 10.1109/AICCSA.2015.7507254. 
  5. H. N. Fejer and N. Omar, "Automatic multi-document Arabic text summarization using clustering and keyphrase extraction," J. Artif. Intell., vol. 8, no. 1, pp. 1-9, 2015, doi: 10.3923/jai.2015.1.9. 
  6. R. Rodriguez-Esteban, "Methods in Biomedical Text Mining," PhD Thesis, Columbia University, 2009. 
  7. and J. R. Blair-Goldensohn, Sasha, Kerry Hannan, Ryan McDonald, Tyler Neylon, George A. Reis, "Building a Sentiment Summarizer for Local Service Reviews," Proc. WWW-2008 Work. NLP Inf. Explos. Era., 2008. 
  8. M. Hameed, F. Tahir, and M. A. Shahzad, "Empirical comparison of sentiment analysis techniques for social media," Int. J. Adv. Appl. Sci., vol. 5, no. 4, pp. 115-123, 2018.  https://doi.org/10.21833/ijaas.2018.04.015
  9. N. Archak, A. Ghose, and P. G. Ipeirotis, "Show me the Money ! Deriving the Pricing Power of Product," Proc. 13th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. - KDD '07, pp. 56-65, 2007, doi: 10.1145/1281192.1281202. 
  10. Y. Chen and J. Xie, "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Manage. Sci., vol. 54, no. 3, pp. 477-491, 2008, doi: 10.1287/mnsc.1070.0810. 
  11. I. El Alaoui, Y. Gahi, R. Messoussi, Y. Chaabi, A. Todoskoff, and A. Kobi, "A novel adaptable approach for sentiment analysis on big social data," J. Big Data, vol. 5, no. 1, 2018, doi: 10.1186/s40537-018-0120-0. 
  12. D. Grabner, M. Zanker, G. Fliedl, and M. Fuchs, "Classification of Customer Reviews based on Sentiment Analysis," Inf. Commun. Technol. Tour. 2012, pp. 460-470, 2012, doi: 10.1007/978-3-7091-1142-0_40. 
  13. Q. Sun, J. Niu, Z. Yao, and H. Yan, "Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level," Eng. Appl. Artif. Intell., vol. 81, pp. 68-78, 2019, doi: 10.1016/j.engappai.2019.02.004. 
  14. J. Jin, P. Ji, and R. Gu, "Identifying comparative customer requirements from product online reviews for competitor analysis," Eng. Appl. Artif. Intell., vol. 49, pp. 61-73, 2016, doi: 10.1016/j.engappai.2015.12.005. 
  15. F. e-M. K. Khan, B.B. Baharudin, A. Khan, "Mining opinion from text documents," Adv. Res. Comput. Commun. Eng., vol. 3, no. 7, pp. 217-222. 
  16. B. Pang and L. Lee, "Opinion mining and sentiment analysis," Found. Trends Inf. Retr., vol. 2, no. 1-2, pp. 1-135, 2008, doi: 10.1561/1500000011. 
  17. M. Z. Asghar, A. Khan, S. Ahmad, and F. M. Kundi, "A Review of Feature Extraction in Sentiment Analysis," J. Basic. Appl. Sci. Res, vol. 4, no. 3, pp. 181-186, 2014. 
  18. A. Kumar, K. Srinivasan, W. H. Cheng, and A. Y. Zomaya, "Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data," Inf. Process. Manag., vol. 57, no. 1, 2020, doi: 10.1016/j.ipm.2019.102141. 
  19. S. Wang, D. Li, Y. Wei, and H. Li, "A Feature Selection Method Based on Fisher 's Discriminant Ratio for Text Sentiment Classification Suge," Springer-Verlag Berlin Heidelb., pp. 88-97, 2009. 
  20. A. Esmin and S. Matwin, "Hierarchical Classification Approach to Emotion Recognition in Twitter Hierarchical Classification Approach to Emotion Recognition in Twitter," 2016, no. March, doi: 10.1109/ICMLA.2012.195. 
  21. M. Ahmad, S. Aftab, M. S. Bashir, N. Hameed, I. Ali, and Z. Nawaz, "SVM optimization for sentiment analysis," Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 4, pp. 393-398, 2018, doi: 10.14569/IJACSA.2018.090455. 
  22. M. Abbas, K. Ali Memon, and A. Aleem Jamali, "Multinomial Naive Bayes Classification Model for Sentiment Analysis," IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 19, no. 3, p. 62, 2019, [Online]. Available: http://paper.ijcsns.org/07_book/201903/20190310.pdf.  https://doi.org/10.pdf
  23. J. Khairnar and M. Kinikar, "Sentiment Analysis Based Mining and Summarizing Using SVM-MapReduce," Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 3, pp. 4081-4085, 2014. 
  24. B. Liu, "Sentiment Analysis and Opinion Mining," Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1-167, 2012, doi: 10.2200/S00416ED1V01Y201204HLT016. 
  25. V. S. D. F. X. Christopher, "Evolving Trends in Conversational Systems with Natural Language Processing," Int. J. Comput. Intell. Informatics, vol. 8, no. 3, pp. 123-129, 2018. 
  26. A. See, "Natural Language Processing with Deep Learning: Natural Language Generation," pp. 1-39, 2019. 
  27. K. Dashtipour, M. Gogate, A. Adeel, H. Larijani, and A. Hussain, "Sentiment analysis of persian movie reviews using deep learning," Entropy, vol. 23, no. 5, 2021, doi: 10.3390/e23050596. 
  28. Q. Tul et al., "Sentiment Analysis Using Deep Learning Techniques: A Review," Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 6, 2017, doi: 10.14569/ijacsa.2017.080657. 
  29. N. C. Dang, M. N. Moreno-Garcia, and F. De la Prieta, "Sentiment analysis based on deep learning: A comparative study," Electron., vol. 9, no. 3, 2020, doi: 10.3390/electronics9030483.
  30. H. Du, X. Xu, X. Cheng, D. Wu, Y. Liu, and Z. Yu, "Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews," Proc. 25th Int. Conf. Companion World Wide Web, pp. 29-30, 2016, doi: 10.1145/2872518.2889403. 
  31. D. Nguyen, K. Vo, D. Pham, M. Nguyen, and T. Quan, "A deep architecture for sentiment analysis of news articles," Adv. Intell. Syst. Comput., vol. 629, pp. 129-140, 2018, doi: 10.1007/978-3-319-61911-8_12. 
  32. X. Ouyang, P. Zhou, C. H. Li, and L. Liu, "Sentiment analysis using convolutional neural network," Proc. - 15th IEEE Int. Conf. Comput. Inf. Technol. CIT 2015, 14th IEEE Int. Conf. Ubiquitous Comput. Commun. IUCC 2015, 13th IEEE Int. Conf. Dependable, Auton. Se, pp. 2359-2364, 2015, doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.349. 
  33. J. Monsen and E. Rennes, "Perceived Text Quality and Readability in Extractive and Abstractive Summaries," Lang. Resour. Eval. Conf. Lr. 2022, no. June, pp. 305-312, 2022.