5G Network in Content Based Emotion Detection by Sentimental Analysis Integrated with Opinion Mining and Deep Learning Architectures
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Abstract
The rapid growth of social networking sites in the Internet era has made them a necessary tool for sharing emotions with the entire world. To extract emotions from text, a variety of tools and approaches are available in fields of opinion mining as well as sentiment analysis. These researches propose novel technique opinion mining based emotion detection from the input social content using deep learning architectures. Here the input has been obtained as social media content based on opinion miningby 5G networks. The input has been processed for noise removal, smoothening and normalization. This processed input has been segmented using Markov model based convolutional neural networks (MMCNN). The segmented data has been classified using Canonical Correlation AnalysisBayesian neural network.An opinion mining method that analyzes statements regarding computer programming and predicts or recognizes their polarity was implemented, along with an earlier module that was integrated into an intelligent learning environment. These three steps made up the creation of the module. We assessed the corpus, text polarity precision, and emotion recognition. Experimental analysis has been carried out for various social media content collected by opinion mining in terms of accuracy, precision, recall, F-1 score, AUC.Proposed technique attained accuracy of 99%, precision of 96%, recall of 96%, F-1 score of 95%, AUC of 89%.
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References
Estrada, M. L. B., Cabada, R. Z., Bustillos, R. O., & Graff, M. (2020). Opinion mining and emotion recognition applied to learning environments. Expert Systems with Applications, 150, 113265.
Vega, M. A., & Todd, M. D. (2022). A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates. Structural Health Monitoring, 21(1), 4-18.
Soong, H. C., Jalil, N. B. A., Ayyasamy, R. K., & Akbar, R. (2019, April). The essential of sentiment analysis and opinion mining in social media: Introduction and survey of the recent approaches and techniques. In 2019 IEEE 9th symposium on computer applications & industrial electronics (ISCAIE) (pp. 272-277). IEEE.
Zvarevashe, K., &Olugbara, O. O. (2018, March). A framework for sentiment analysis with opinion mining of hotel reviews. In 2018 Conference on information communications technology and society (ICTAS) (pp. 1-4). IEEE.
Ahire, V., &Borse, S. (2022). Emotion detection from social media using machine learning techniques: a survey. In Applied Information Processing Systems (pp. 83-92). Springer, Singapore.
Raza, M. R., Hussain, W., Tanyıldızı, E., &Varol, A. (2021, June). Sentiment analysis using deep learning in cloud. In 2021 9th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-5). IEEE.
Yuzhong, H. (2021). Students’ emotional analysis on ideological and political teaching classes based on artificial intelligence and data mining. Journal of Intelligent & Fuzzy Systems, 40(2), 3801-3809.
Aiswaryadevi, V. J., Kiruthika, S., Priyanka, G., Nataraj, N., & Sruthi, M. S. (2021). Effective Multimodal Opinion Mining Framework Using Ensemble Learning Technique for Disease Risk Prediction. In Inventive Computation and Information Technologies (pp. 925-933). Springer, Singapore.
Nassif, A. B., Elnagar, A., Shahin, I., &Henno, S. (2021). Deep learning for Arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing, 98, 106836.
PM, K. R. (2022). Sentiment analysis, opinion mining and topic modelling of epics and novels using machine learning techniques. Materials Today: Proceedings, 51, 576-584.
Zhu, Z. (2022). Deep Learning for Chinese Language Sentiment Extraction and Analysis. Mathematical Problems in Engineering, 2022.
Kaladevi, P., &Thyagarajah, K. (2021). RETRACTED ARTICLE: Integrated CNN-and LSTM-DNN-based sentiment analysis over big social data for opinion mining. Behaviour & Information Technology, 40(9), XI-XIX.
Ajitha, P., Sivasangari, A., Immanuel Rajkumar, R., &Poonguzhali, S. (2021). Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms. Journal of Intelligent & Fuzzy Systems, 40(4), 6375-6383.
Kaur, P. (2022). Sentiment analysis using web scraping for live news data with machine learning algorithms. Materials Today: Proceedings.
Mehrotra, R., Garg, O., Gupta, S., & Singh, A. (2022). Opinion Mining of Pandemic Using Machine Learning. In Advances in Data and Information Sciences (pp. 225-231). Springer, Singapore.
Hussein, D. J., Rashad, M. N., Mirza, K. I., & Hussein, D. L. (2022). Machine Learning Approach to Sentiment Analysis in Data Mining. Passer Journal of Basic and Applied Sciences, 4(1), 71-77.
Golchin, B., &Riahi, N. (2021). Emotion detection in twitter messages using combination of long short-term memory and convolutional deep neural networks. International Journal of Computer and Information Engineering, 15(9), 578-585.
Diwan, T., &Tembhurne, J. V. (2022). Sentiment analysis: a convolutional neural networks perspective. Multimedia Tools and Applications, 1-25.
De, A., & Mishra, S. (2022). Augmented Intelligence in Mental Health Care: Sentiment Analysis and Emotion Detection with Health Care Perspective. Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis, 205-235.
Biradar, S. H., Gorabal, J. V., & Gupta, G. (2022). Machine learning tool for exploring sentiment analysis on twitter data. Materials Today: Proceedings, 56, 1927-1934.
Jain, P. K., Saravanan, V., & Pamula, R. (2021). A hybrid CNN-LSTM: A deep learning approach for consumer sentiment analysis using qualitative user-generated contents. Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1-15.
Basiri, M. E., Nemati, S., Abdar, M., Asadi, S., &Acharrya, U. R. (2021). A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 228, 107242.