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An Optimized Deep Neural Aspect Based Framework for Sentiment Classification

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A Correction to this article was published on 21 December 2022

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Abstract

The current digitalized world has emerged with the advances of big data, such as online learning, business, marketing, etc. So reviewing the customer opinion in each field is more critical in enabling smart services. Several neural models are implemented diversely to characterize the customer opinion from the gained review of the user or customer. But, those models have met some difficulties detecting the aspect terms and sentiment values. So, this proposed article has planned to design a novel Strawberry Deep Belief Neural Mechanism (SDBNM) to classify the aspect and sentiment values from the trained datasets. In addition, to evaluate the performance of the designed approach, three datasets are adopted: product, trip review, and Twitter data. Initially, after the data training process, the error is extracted from the database in the preprocessing model of SDBNM. Hereafter, the error-free data is imported to the dense frame of the SDBNM to specify the aspect terms and sentiment values. At last, the parameters are estimated and compared with old approaches and have earned the best results for classification rate.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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References

  1. Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., & Karthick, G. (2021). Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM). Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08580-3

    Article  Google Scholar 

  2. Mahalakshmi, P., & Fatima, N. S. (2021). Ensembling of text and images using deep convolutional neural networks for intelligent information retrieval. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08211-x

    Article  Google Scholar 

  3. BalaAnand, M., Karthikeyan, N., & Karthik, S. (2019). Envisioning social media information for big data using big vision schemes in wireless environment. Wireless Personal Communications, 109, 777–796. https://doi.org/10.1007/s11277-019-06590-w

    Article  Google Scholar 

  4. Das, D. (2018). Positive and negative link prediction algorithm based on sentiment analysis in large social networks. Wireless Personal Communications, 102, 2183–2198. https://doi.org/10.1007/s11277-018-5499-6

    Article  Google Scholar 

  5. Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. (2020). Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2020.3038167

    Article  Google Scholar 

  6. Liu, B. (2020). Text sentiment analysis based on CBOW model and deep learning in big data environment. Journal of Ambient Intelligence and Humanized Computing, 11(2), 451–458. https://doi.org/10.1007/s12652-018-1095-6

    Article  Google Scholar 

  7. Kushwah, S., & Das, S. (2020). Sentiment analysis of big-data in healthcare: issue and challenges. In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), IEEE. https://doi.org/10.1109/ICCCA49541.2020.9250841

  8. Sun, X., & He, J. (2020). A novel approach to generate a large scale of supervised data for short text sentiment analysis. Multimedia Tools and Applications, 79(9), 5439–5459. https://doi.org/10.1007/s11042-018-5748-4

    Article  Google Scholar 

  9. Xia, R., Jiang, J., & He, H. (2017). Distantly supervised lifelong learning for large-scale social media sentiment analysis. IEEE Transactions on Affective Computing, 8(4), 480–491. https://doi.org/10.1109/TAFFC.2017.2771234

    Article  Google Scholar 

  10. Muhammad, A., Wiratunga, N., & Lothian, R. (2016). Contextual sentiment analysis for social media genres. Knowledge-based Systems, 108, 92–101. https://doi.org/10.1016/j.knosys.2016.05.032

    Article  Google Scholar 

  11. Geetha, R., & Thilagam, T. (2021). A review on the effectiveness of machine learning and deep learning algorithms for cyber security. Archives of Computational Methods in Engineering, 28(4), 2861–2879. https://doi.org/10.1007/s11831-020-09478-2

    Article  MathSciNet  Google Scholar 

  12. Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big data: Deep Learning for financial sentiment analysis. Journal of Big Data, 5(1), 1–25. https://doi.org/10.1186/s40537-017-0111-6

    Article  Google Scholar 

  13. Agüero-Torales, M. M., Salas, J. I. A., & López-Herrera, A. G. (2021). Deep learning and multilingual sentiment analysis on social media data: An overview. Applied Soft Computing, 107, 107373. https://doi.org/10.1016/j.asoc.2021.107373

    Article  Google Scholar 

  14. Ruz, G. A., Henríquez, P. A., & Mascareño, A. (2020). Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92–104. https://doi.org/10.1016/j.future.2020.01.005

    Article  Google Scholar 

  15. Naseem, U., Razzak, I., Khushi, M., Eklund, P. W., & Kim, J. (2021). Covidsenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis. IEEE Transactions on Computational Social Systems, 8(4), 1003–1015. https://doi.org/10.1109/TCSS.2021.3051189

    Article  Google Scholar 

  16. Khan, M., & Malviya, A. (2020). Big data approach for sentiment analysis of twitter data using Hadoop framework and deep learning. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), IEEE. https://doi.org/10.1109/ic-ETITE47903.2020.201

  17. Meškelė, D., & Frasincar, F. (2020). ALDONAr: A hybrid solution for sentence-level aspect-based sentiment analysis using a lexicalized domain ontology and a regularized neural attention model. Information Processing & Management, 57(3), 102211. https://doi.org/10.1016/j.ipm.2020.102211

    Article  Google Scholar 

  18. Nandal, N., Tanwar, R., & Pruthi, J. (2020). Machine learning based aspect level sentiment analysis for Amazon products. Spatial Information Research, 28(5), 601–607. https://doi.org/10.1007/s41324-020-00320-2

    Article  Google Scholar 

  19. Phan, M. H., & Ogunbona, P. O. (2020). Modelling context and syntactical features for aspect-based sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 3211–3220). https://aclanthology.org/2020.acl-main.293

  20. Kundu, S., & Chakraborti, S. (2020). A comparative study of online consumer reviews of Apple iPhone across Amazon, Twitter and MouthShut platforms. Electronic Commerce Research. https://doi.org/10.1007/s10660-020-09429-w

    Article  Google Scholar 

  21. Kothalawala, M., & Thelijjagoda, S. (2020). Aspect-based sentiment analysis on hair care product reviews. In: 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), IEEE. https://doi.org/10.1109/SCSE49731.2020.9313040

  22. Maheswari, S. U., & Dhenakaran, S. S. (2020). Aspect based Fuzzy Logic Sentiment Analysis on Social Media Big Data. In: 2020 International Conference on Communication and Signal Processing (ICCSP), IEEE. https://doi.org/10.1109/ICCSP48568.2020.9182174

  23. Tammina, S., & Annareddy, S. (2020). Sentiment analysis on customer reviews using convolutional neural network. In: 2020 International Conference on Computer Communication and Informatics (ICCCI), IEEE. https://doi.org/10.1109/ICCCI48352.2020.9104086

  24. Yadav, A., Agarwal, A., & Vishwakarma, D. K. (2019). XRA-net framework for visual sentiments analysis. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), IEEE. https://doi.org/10.1109/BigMM.2019.00-22

  25. Yadav, A., & Vishwakarma, D. K. (2020). A deep learning architecture of RA-DLNet for visual sentiment analysis. Multimedia Systems, 26(4), 431–451. https://doi.org/10.1007/s00530-020-00656-7

    Article  Google Scholar 

  26. Yadav, A., & Vishwakarma, D. K. (2020). A deep multi-level attentive network for multimodal sentiment analysis. ACM Transactions on Multimidia Computing Communications and Applications. https://doi.org/10.1145/3517139

    Article  Google Scholar 

  27. Mowlaei, M. E., Abadeh, M. S., & Keshavarz, H. (2020). Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Systems with Applications, 148, 113234. https://doi.org/10.1016/j.eswa.2020.113234

    Article  Google Scholar 

  28. Alamanda, M. S. (2020). Aspect-based sentiment analysis search engine for social media data. CSI Transactions on ICT, 8, 193–197. https://doi.org/10.1007/s40012-020-00295-3

    Article  Google Scholar 

  29. Kastrati, Z., Imran, A. S., & Kurti, A. (2020). Weakly supervised framework for aspect-based sentiment analysis on students’ reviews of MOOCs. IEEE Access, 8, 106799–106810. https://doi.org/10.1109/ACCESS.2020.3000739

    Article  Google Scholar 

  30. Li, N., Chow, C. Y., & Zhang, J. D. (2020). SEML: A semi-supervised multi-task learning framework for aspect-based sentiment analysis. IEEE Access, 8, 189287–189297. https://doi.org/10.1109/ACCESS.2020.3031665

    Article  Google Scholar 

  31. Jia, Z., Bai, X., & Pang, S. (2020). Hierarchical gated deep memory network with position-aware for aspect-based sentiment analysis. IEEE Access, 8, 136340–136347. https://doi.org/10.1109/ACCESS.2020.3011318

    Article  Google Scholar 

  32. Truşcǎ, M. M., Wassenberg, D., Frasincar, F., & Dekker, R. (2020). A hybrid approach for aspect-based sentiment analysis using deep contextual word embeddings and hierarchical attention. In International Conference on Web Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-50578-3_25

  33. Zhou, J., Huang, J. X., Hu, Q. V., & He, L. (2020). Is position important? Deep multi-task learning for aspect-based sentiment analysis. Applied Intelligence, 50, 3367–3378. https://doi.org/10.1007/s10489-020-01760-x

    Article  Google Scholar 

  34. Xu, Q., Zhu, L., Dai, T., & Yan, C. (2020). Aspect-based sentiment classification with multi-attention network. Neurocomputing, 388, 135–143. https://doi.org/10.1016/j.neucom.2020.01.024

    Article  Google Scholar 

  35. Jiang, N., Tian, F., Li, J., Yuan, X., & Zheng, J. (2020). MAN: Mutual attention neural networks model for aspect-level sentiment classification in SIoT. IEEE Internet of Things Journal, 7(4), 2901–2913. https://doi.org/10.1109/JIOT.2020.2963927

    Article  Google Scholar 

  36. Afzaal, M., Usman, M., & Fong, A. (2019). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65(2), 233–242. https://doi.org/10.1109/TCE.2019.2908944

    Article  Google Scholar 

  37. Kumar, R., Pannu, H. S., & Malhi, A. K. (2020). Aspect-based sentiment analysis using deep networks and stochastic optimization. Neural Computing and Applications, 32(8), 3221–3235. https://doi.org/10.1007/s00521-019-04105-z

    Article  Google Scholar 

  38. Alamoudi, E. S., & Alghamdi, N. S. (2021). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems. https://doi.org/10.1080/12460125.2020.1864106

    Article  Google Scholar 

  39. Behera, R. K., Jena, M., Rath, S. K., & Misra, S. (2021). Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Information Processing & Management, 58(1), 102435. https://doi.org/10.1016/j.ipm.2020.102435

    Article  Google Scholar 

  40. Phan, H. T., Tran, V. C., Nguyen, N. T., & Hwang, D. (2020). Improving the performance of sentiment analysis of tweets containing fuzzy sentiment using the feature ensemble model. IEEE Access, 8, 14630–14641. https://doi.org/10.1109/ACCESS.2019.2963702

    Article  Google Scholar 

  41. Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access, 8, 23522–23530. https://doi.org/10.1109/ACCESS.2020.2969854

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Centurion University of Technology and Management, Paralakhemundi, Odisha, 761211, India

    N. Lakshmidevi

  2. Departement of IT, Aditya Engineering College, Surampalem, Andhra Pradesh, 533437, India

    M. Vamsikrishna

  3. Department of Computer Science and Engineering, School of Engineering & Technology, Centurion University of Technology & Management, Paralakhemundi, Odisha, 761211, India

    S. S. Nayak

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  1. N. Lakshmidevi
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Lakshmidevi, N., Vamsikrishna, M. & Nayak, S.S. An Optimized Deep Neural Aspect Based Framework for Sentiment Classification. Wireless Pers Commun 128, 2953–2979 (2023). https://doi.org/10.1007/s11277-022-10081-w

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