1
Department of Computer Science and Engineering, RIMT University, Gobindgarh, Punjab, India
(lishayugal26@gmail.com)
2
Department of Computer Science and Engineering, Sharda University, Andijan, Uzbekistan
(sureshkaswan@gmail.com)
3
Department of Computer Science and Engineering, RIMT University, Gobindgarh, Punjab, India
(bsbhatia29@hotmail.com)
4
Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, India; IEEE Senior Member, Symbiosis Institute of Technology, Pune, India
(aditi.sharma@ieee.org)
Abstract :
In recent years, most of the research exhibits in the field of Education 4.0 Training Systems (ETS) and Industry 4.0 Training Systems (ITS) that has the ability to learn the behavior of the learners, interns, or trainees. Understanding the feelings and emotions of learners toward learning is essential for creating a practical and exciting learning experience. Patience-emotions detection and sentiments analysis have emerged as an integral part of the understanding of the behaviors of learners, thus there is a need to expand the overall educational or training process in academics and industries. This model enables teachers, trainers and instruction designers to obtain valuable information, which can be used to optimize teaching strategies to improve learning outcomes. To achieve this goal with IoT-enabled objects, an Academician can create a more personalized and effective learning environment for students, trainees and interns. A novel emulated framework is designed and implemented with IoT and machine learning techniques to analyze the performance of learners. The model receives feedback from 1000 learners using IoT devices and analysis the missing information in the learning systems, that missing information lacked effective learning. This emulated framework analyzes the performance of the model. A novel and innovative early warning system is also created to send the warning on WhatsApp or email to several users in a single shot, when achieving certain goals such as file’s size limits and so on. In this research SVM, MCC, NLP and CNN machine learning algorithms are applied to detect students’ feelings and emotions to track the feedback via IoT enabled system.
Keywords :
Machine Learning; Sentiment Analysis; Feedback system; Academia 4.0 , Industries 4.0 , Learning based materials; Emotion Analysis; Teaching Strategies; Internet of things (IoT); Natural Language Processing (NLP)
References :
[1] Ahuja R, Chug A, Kohli S, Gupta S, Ahuja P (2019) The impact of features extraction on the sentiment analysis. Procedia Comput Sci 152:341–348
[2] Archana Rao PN, Baglodi K (2017) Role of sentiment analysis in education sector in the era of big data: a survey. Int J Latest Trends Eng Technol 22–24
[3] Arora M, Kansal V (2019) Character level embedding with deep convolutional neural network for text normalization of unstructured data for twitter sentiment analysis.
[4] Arulmurugan R, Sabarmathi K, Anandakumar H (2019) Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Comput 22(1):1199–1209 Ahmad Z, Jindal R, Ekbal A, Bhattachharyya P (2020) Borrow from rich cousin: transfer learning for emotion detection using cross lingual embedding. Expert Syst Appl 139:112851
[5] Ahuja R, Chug A, Kohli S, Gupta S, Ahuja P (2019) The impact of features extraction on the sentiment analysis. Procedia Comput Sci 152:341–348
[6] Archana Rao PN, Baglodi K (2017) Role of sentiment analysis in education sector in the era of big data: a survey. Int J Latest Trends Eng Technol 22–24
[7] Arora M, Kansal V (2019) Character level embedding with deep convolutional neural network for text normalization of unstructured data for twitter sentiment analysis.
[8] Arulmurugan R, Sabarmathi K, Anandakumar H (2019) Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Comput 22(1):1199–1209
[9] Bandhakavi A, Wiratunga N, Padmanabhan D, Massie S (2017) Lexicon based feature extraction for emotion text classification. Pattern Recogn Lett 93:133–142
[10] Batbaatar E, Li M, Ryu KH (2019) Semantic-emotion neural network for emotion recognition from text. IEEE Access 7:111866–111878
[11] Bhardwaj A, Narayan Y, Dutta M et al (2015) Sentiment analysis for Indian stock market prediction using sensex and nifty. Procedia Comput Sci 70:85–91
[12] Bhaskar J, Sruthi K, Nedungadi P (2015) Hybrid approach for emotion classification of audio conversation based on text and speech mining. Procedia Comput Sci 46:635–643
[13] Prashant K. Jamwal, Aibek Niyetkaliyev, Shahid Hussain, Aditi Sharma, Paulette Van Vliet, Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home, MethodsX, Volume 11,2023,102312,ISSN 2215-0161,https://doi.org/10.1016/j.mex.2023.102312.
[14] Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P (2019) Understanding emotions in text using deep learning and big data. Chowanda A, Sutoyo R, Tanachutiwat S et al (2021) Exploring text-based emotions recognition machine learning techniques on social media conversation. Procedia Comput Sci 179:821–828
[15] Dashtipour K, Gogate M, Li J, Jiang F, Kong B, Hussain A (2020) A hybrid Persian sentiment analysis framework: integrating dependency grammar-based rules and deep neural networks. Neurocomputing 380:1–10
[16] Devi Sri Nandhini M, Pradeep G (2020) A hybrid co-occurrence and ranking-based approach for the detection of implicit aspects in aspect-based sentiment analysis. SN Comput Sci 1:1–9
[17] Kumar, N., & Sharma, A. (2017). Sentimental analysis for political activities from social media data analytics.
[18] Garcia K, Berton L (2021) Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Appl Soft Comput 101:107057
[19] Qusay Abboodi Ali,Noor M. Sahab. (2023). Interactive Design of a Virtual Classroom Simulation Model Based on Multimedia Applications to Improve the Teaching and Learning Process in the Tikrit University Environment. Fusion: Practice and Applications, 12 ( 2 ), 206-216.
[20] Hazarika D, Poria S, Zimmermann R, Mihalcea R (2020) Conversational transfer learning for emotion recognition. Inf Fusion 65:1–12
[21] Kumar N., A. Sharma A., “A spoofing security approach for facial biometric data authentication in unconstraint environment,” In: Pati B., Panigrahi C., Misra S., Pujari A., Bakshi S. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol. 713, 2019, Springer, Singapore. https://doi.org/10.1007/978-981-13-1708-8_40
[22] Mukherjee P, Badr Y, Doppalapudi S, Srinivasan SM, Sangwan RS, Sharma R (2021) Effect of negation in sentences on sentiment analysis and polarity detection. Procedia Comput Sci 185:370–379
[23] Nandal N, Tanwar R, Pruthi J (2020) Machine learning-based aspect-level sentiment analysis for Amazon products. Spat Inf Res 28(5):601–607
[24] Nandwani P, Verma R. A review of sentiment analysis and emotion detection from text. Soc Netw Anal Min. 2021;11(1):81. doi: 10.1007/s13278-021-00776-6. Epub 2021 Aug 28. PMID: 34484462; PMCID: PMC8402961.
[25] Anita Venugopal, Aditi Sharma,F. Abdul Munaim Al Rawas,Rama Devi S.. "Enhancing Fusion Teaching based Research from the Student Perspective." Fusion: Practice and Applications, Vol. 12, No. 2, 2023 ,PP. 109-119.
[26] R. Dash, T. N. Nguyen, K. Cengiz, A. Sharma, “FTSVR: Fine-tuned support vector regression model for stock predictions,” Neural Computing and Applications, 2021.https://10.1007/s00521-021-05842-w
[27] Rao G, Huang W, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49–57
[28] Goar, V., Sharma, A., Yadav, N.S. et al. IoT-Based Smart Mask Protection against the Waves of COVID-19. J Ambient Intell Human Comput (2022). https://doi.org/10.1007/s12652-022-04395-7
[29] Sangeetha K, Prabha D (2020) Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM. J Ambient Intell Hum Comput 12:4117–4126
[30] Reem Atassi, Aditi Sharma. "Intelligent Traffic Management using IoT and Machine Learning." Journal of Intelligent Systems and Internet of Things, Vol. 8, No. 2, 2023 ,PP. 08-19.
[31] Sasidhar TT, Premjith B, Soman K (2020) Emotion detection in Hinglish (Hindi + English) code-mixed social media text. Procedia Comput Sci 171:1346–1352
[32] Gajender Kumar,Vinod Patidar,Prolay Biswas,Mukta Patel,Chaur Singh Rajput,Anita Venugopal,Aditi Sharma. "IOT enabled Intelligent featured imaging Bone Fractured Detection System." Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 2, 2023 ,PP. 08-22.
[33] Shirsat VS, Jagdale RS, Deshmukh SN (2019) Sentence-level sentiment identification and calculation from news articles using machine learning techniques. In: Computing, communication and signal processing. Springer, pp 371–376
[34] Mahmoud A. Zaher,Nashaat K. ElGhitany. Intelligent System for Body Fat Percentage Prediction. Journal of Intelligent Systems and Internet of Things, (2021); 5 ( 2 ): 62-71.
[35] Saeed M. Aljaberi,Ahmed N. Al-Masri. Automated Deep Learning based Video Summarization Approach for Forest Fire Detection. Journal of Intelligent Systems and Internet of Things, (2021); 5 ( 2 ): 54-61.
[36] Zeena N. Al-kateeb,Dhuha Basheer Abdullah. A Smart Architecture Leveraging Fog Computing Fusion and Ensemble Learning for Prediction of Gestational Diabetes. Fusion: Practice and Applications, (2023); 12 ( 2 ): 70-87.
[37] Shrivastava K, Kumar S, Jain DK (2019) An effective approach for emotion detection in multimedia text data using sequence-based convolutional neural network. Multim Tools Appl 78(20):29607–29639
[38] Singh M, Jakhar AK, Pandey S (2021) Sentiment analysis on the impact of coronavirus in social life using the BERT model. Soc Netw Anal Min 11(1):1–11
[39] Souma W, Vodenska I, Aoyama H (2019) Enhanced news sentiment analysis using deep learning methods. J Comput Soc Sci 2(1):33–46
[40] Ahmed M. Daoud ,Khlid M. Hosny ,Ehab R. Mohamed. Building a New Semantic Social Network Using Semantic Web-Based Techniques. Fusion: Practice and Applications, (2021); 3 ( 2 ): 54-65.
[41] Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S., Pantic, M. (2017). A survey of multimodal sentiment analysis. Image and Vision Computing, 65, 3–14. https://doi.org/10.1016/j.imavis.2017.08.003
| Style | # |
|---|---|
| MLA | Lisha Yugal, Suresh Kaswan, B. S. Bhatia, Aditi Sharma. "IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0." Journal of Intelligent Systems and Internet of Things, Vol. 10, No. 2, 2023 ,PP. 36-75 (Doi : https://doi.org/10.54216/JISIoT.100206) |
| APA | Lisha Yugal, Suresh Kaswan, B. S. Bhatia, Aditi Sharma. (2023). IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 36-75 (Doi : https://doi.org/10.54216/JISIoT.100206) |
| Chicago | Lisha Yugal, Suresh Kaswan, B. S. Bhatia, Aditi Sharma. "IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0." Journal of Journal of Intelligent Systems and Internet of Things, 10 no. 2 (2023): 36-75 (Doi : https://doi.org/10.54216/JISIoT.100206) |
| Harvard | Lisha Yugal, Suresh Kaswan, B. S. Bhatia, Aditi Sharma. (2023). IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 36-75 (Doi : https://doi.org/10.54216/JISIoT.100206) |
| Vancouver | Lisha Yugal, Suresh Kaswan, B. S. Bhatia, Aditi Sharma. IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 10 ( 2 ): 36-75 (Doi : https://doi.org/10.54216/JISIoT.100206) |
| IEEE | Lisha Yugal, Suresh Kaswan, B. S. Bhatia, Aditi Sharma, IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 10 , No. 2 , (2023) : 36-75 (Doi : https://doi.org/10.54216/JISIoT.100206) |