Journal Press India^®

Author Details ( * ) denotes Corresponding author

Textual data mining is used to anticipate the sentiment of a user based on a similar book. Using conditional probability distributions, the rating similarity between books can be quantified by textual mining. In supervised learning, the input and output data are sent to the machine learning model in tandem to maximize accuracy. In this paper, a cloud-based suggested system is incorporated which detects and recommends similar types of content based on the ranking of books. A cloud recommendation system is a means of determining which service is best suited to the user’s tastes and requirements. The work uses specific formats such as graphics or text, and the networks built throughout the process may help uncover the links between the words. Textual mining is used to calculate book-ranging similarities and recommendations. From the comparison result, the proposed logistic regression technique has maximum accuracy which is 82% as compared to the existing technique The model shows all recommended books that are like the input book are displayed, while all other types of books are hidden from view. Also, positive, or negative masking can be determined by comparing the intensity of the extracted images.

Keywords

Sentiments Analysis; Cloud-based Recommendation System; Contextual Mining; Text Mining; Supervised Learning

1. Yu, L.-C., Jheng-Long, W., Pei-Chann, C. & Hsuan-Shou, C. (2013). Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowledge-Based Systems, 41,     89-97.
2. Ligthart, A., Cagatay, C. & Bedir, T. (2021). Systematic reviews in sentiment analysis: A tertiary study. Artificial Intelligence Review, 54(7), 4997-5053.
3. Hagenau, M., Michael, L. & Dirk, N. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems, 55(3), 685-697.
4. Maks, I. & Piek, V. (2012). A lexicon model for deep sentiment analysis and opinion mining applications. Decision Support Systems, 53(4), 680-688.
5. Ravi, K. & Vadlamani, R. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches, and applications. Knowledge-based Systems, 89,     14-46.
6. Heidari, M. & Setareh, R. (2020). Using transfer learning approach to implement convolutional neural network model to recommend airline tickets by using online reviews. In 2020 15^th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA, pp. 1-6). IEEE.
7. Zirpe, S. & Bela, J. (2017). Polarity shift detection approaches in sentiment analysis: A survey. In 2017 International Conference on Inventive Systems and Control (ICISC), pp. 1-5. IEEE.
8. Heidari, M. & Setareh, R. (2020). Semantic convolutional neural network model for safe business investment by using bert. In 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1-6. IEEE.
9. Wang, H. & Jorge, A. C. (2015). Sentiment expression via emoticons on social media. In 2015 IEEE International Conference on Big Data, pp. 2404-2408. IEEE.
10. Heidari, M., James, H. J. & Ozlem, U. (2020). Deep contextualized word embedding for text-based online user profiling to detect social bots on twitter. In 2020 International Conference on Data Mining Workshops (ICDMW), pp.        480-487. IEEE.
11. Lunn, S., Jia, Z. & Monique, R. (2020). Utilizing web scraping and natural language processing to better inform pedagogical practice. In 2020 IEEE Frontiers in Education Conference (FIE), pp. 1-9. IEEE.
12. Rezapour, R., Lufan, W., Omid, A. & Jana, D. (2017). Identifying the overlap between election result and candidates’ ranking based on hashtag-enhanced, lexicon-based sentiment analysis. In 2017 IEEE 11^th International Conference on Semantic Computing (ICSC), pp. 93-96. IEEE.
13. Heidari, M. & James, H. J. (2020). Using bert to extract topic-independent sentiment features for social media bot detection. In 2020 11^th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0542-0547. IEEE.
14. Heidari, M., Samira, Z., Brett, B. & Setareh, R. (2021). Ontology creation model based on attention mechanism for a specific business domain. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1-5. IEEE.
15. Farahani, A., Sahar, V., Khaled, R. & Hamid, R. A. (2021). A brief review of domain adaptation. Advances in Data Science and Information Engineering, 877-894. Retrieved from https://www.researchgate.net/publication/344551768_A_ Brief_Review_of_Domain_Adaptation
16. Yang, Z., Diyi, Y., Chris, D., Xiaodong, H., Alex, S., & Eduard, H. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480-1489.
17. Gao, M., Renli, T., Junhao, W., Qingyu, X., Bin, L., & Linda, Y. (2015). Item anomaly detection based on dynamic partition for time series in recommender systems. PloS one, 10(8), e0135155.
18. Pandarachalil, R., Selvaraju, S. & Mahalakshmi, G. S. (2015). Twitter sentiment analysis for large-scale data: An unsupervised approach. Cognitive Computation, 7(2), 254-262.
19. Tsytsarau, M. & Themis, P. (2012). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24(3), 478-514.
20. Alaei, A. R., Susanne, B. & Bela, S. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
21. Smith, A. N., Eileen, F. & Chen, Y. (2012). How does brand-related user-generated content differ across YouTube, Facebook, and Twitter. Journal of Interactive Marketing, 26(2), 102-113.
22. Suresh, P. & Gurumoorthy, K. (2022). Mining of customer review feedback using sentiment analysis for smart phone product. In International Conference on Computing, Communication, Electrical and Biomedical Systems, (pp. 247-259). Springer, Cham.
23. Jeon, W., Yebin, L. & Youngjung, G. (2021). Airline service quality evaluation based on customer review using machine learning approach and sentiment analysis. The Journal of Society for e-Business Studies, 26(4), 15-36.
24. Park, S., Cho, J., Park, K. & Shin, H. (2021). Customer sentiment analysis with more sensibility. Engineering Applications of Artificial Intelligence, 104, 104356.
25. Bera, A., Mrinal, K. G. & Dibyendu, K. P. (2021). Sentiment analysis of multilingual tweets based on Natural Language Processing (NLP). International Journal of System Dynamics Applications (IJSDA), 10(4), 1-12.
26. Deepa, D. (2021). Bidirectional encoder representations from transformers (BERT) language model for sentiment analysis task. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(7), 1708-1721.
27. Devlin, J., Ming-Wei, C., Kenton, L. & Kristina, T. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
28. Alaparthi, S. & Manit, M. (2021). BERT: A sentiment analysis odyssey. Journal of Marketing Analytics, 9(2), 118-126.
29. Fatemeh, H. & Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(3),   1495-1545.
30. Alamoudi, E. S. & Norah, S. A. (2021). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 30(2-3), 259-281.
31. Ghannay, S., Benoit, F., Yannick, E. & Nathalie, C. (2016). Word embedding evaluation and combination. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pp. 300-305.
32. Dang, N. C., Moreno-García, M. N. & Fernando, D. P. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), 483.
33. Lien, A. K., Lars, M. R., Hans, P. F. T. & Maryam, E. (2022). OSN dashboard tool for sentiment analysis. arXiv preprint arXiv:2206.06935.
34. Dadhich, A. & Thankachan, B. (2021). Social & juristic challenges of AI for opinion mining approaches on Amazon & Flipkart product reviews using machine learning algorithms. SN Computer Science, 2(3), 1–21.
35. Salido, O., Martin, G., Luis-Felipe, R. & Gutierrez-Garcia, J. O. (2020). Towards emotion recognition from contextual information using machine learning. Journal of Ambient Intelligence and Humanized Computing, 11(8), 3187-3207.
36. Chandra, Y. & Antoreep, J. (2020). Sentiment analysis using machine learning and deep learning. In 2020 7^th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1-4. IEEE.
37. Abdul, R., Asim, M., Ali, Z., Qadri, S., Mumtaz, I., Khan, D. M. & Niaz, Q. (2019). Text sentiment analysis using frequency-based vigorous features. China Communications, 16(12), 145-153.
38.  Osman, N. A., Noah, S. A. M. & Darwich, M. (2019). Contextual sentiment-based recommender system to provide recommendation in the electronic products domain. International Journal of Machine Learning and Computing, 9(4),         425-431.
39. Hasan, M., Ishrak, I. & Hasan, K. M. A. (2019). Sentiment analysis using out of core learning. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-6. IEEE.
40. Hassan, A. & Ausif, M. (2018). Convolutional recurrent deep learning model for sentence classification. IEEE Access, 6, 13949-13957.
41. Jianqiang, Z., Gui, X. & Zhang, X. (2018). Deep convolution neural networks for twitter sentiment analysis. IEEE Access, 6, 23253-23260.
42. Sun, X. & Jiajin, H. (2018). A novel approach to generate a large scale of supervised data for short text sentiment analysis. Multimedia Tools and Applications, 1-21.
43. Choudhary, N., Singh, R., Bindlish, I. & Shrivastava, M. (2018). Neural network architecture for credibility assessment of textual claims. arXiv preprint arXiv:1803.10547.
44. Sabuj, M. S., Zakia, A. & Hasan, K. M. (2017). Opinion mining using support vector machine with web based diverse data. In International Conference on Pattern Recognition and Machine Intelligence, pp. 673-678. Springer, Cham.
45. Ramteke, J., Shah, S., Godhia, D. & Shaikh, A. (2016). Election result prediction using Twitter sentiment analysis. In 2016 International Conference on Inventive Computation Technologies (ICICT), 1, 1-5. IEEE.
46. Bhardwaj, A., Narayan, Y. & Dutta, M. (2015). Sentiment analysis for Indian stock market prediction using Sensex and nifty. Procedia Computer Science, 70, 85-91.
47. Rohani, V. A. & Shayaa, S. (2015). Utilizing machine learning in sentiment analysis: SentiRobo approach. In 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET), pp. 263-267. IEEE.
48. Lima, A., Carolina, E. S., Leandro, N. C. & Juan, M. C. (2015). A polarity analysis framework for Twitter messages. Applied Mathematics and Computation, 270, 756-767.
49. Hasan, K. M. A. & Mosiur, R. (2014). Sentiment detection from bangla text using contextual valency analysis. In 2014 17^th International Conference on Computer and Information Technology (ICCIT), pp. 292-295. IEEE.
50. Hutto, C. & Eric, G. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225.
51. Tang, D., Furu, W., Nan, Y., Ming, Z., Ting, L. & Bing, Q. (2014). Learning sentiment-specific word embedding for twitter sentiment classification. ACL, 1, 1555-1565.
52. Venkata, K. P., Misra, S., Joshi, D. & Obaidat, M. S. (2013). Learning automata-based sentiment analysis for recommender system on cloud. In 2013 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1-5. IEEE.
53. Li, S., Zhongqing, W., Sophia, Y. M. L. & Chu-Ren, H. (2013). Sentiment classification with polarity shifting detection. In 2013 International conference on Asian language processing, pp. 129-132. IEEE.
54. Chen, S., Chao, P., Linsen, C. & Lanying, G. (2018). A deep neural network model for target-based sentiment analysis. In 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. IEEE.
55. Hu, Z., Peng, J., & Zhao, H. (2021). Dynamic neural orthogonal mapping for fault detection. International Journal of Machine Learning and Cybernetics, 12(5), 1501-1516.