Impact of social media on the evolution of English semantics through linguistic analysis
Abstract
Social media (SM) influences social interaction in the age of digital media, impacting how languages develop. Since these networks play a role in daily life, they create new words and conceptual frameworks that define our contemporary society. The current investigation investigates Twitter, Facebook, and Reddit SM posts applying textual extraction. The seven-year temporal sample demonstrates significant semantic change caused by society and technology. The analysis notices the importance of new words, phrase meaning evolving, and sentiment changes in SM users’ English usage, proving their adaptability. The growing popularity of phrases like eavesdropping and doom-scrolling indicated how SM and daily life impact. This investigation distinguishes each platform’s unique linguistic features and digital developments by understanding language flow and leading research in the future.
References
[1]Alnuaim, A. A., Zakariah, M., Alhadlaq, A., et al. (2022). Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks. Computational Intelligence and Neuroscience, 2022, 1–16. https://doi.org/10.1155/2022/7463091
[2]Armon-Lotem, S., Rose, K., & Altman, C. (2020). The development of English as a heritage language: The role of chronological age and age of onset of bilingualism. First Language, 41(1), 67–89. https://doi.org/10.1177/0142723720929810
[3]Ashok Kumar, P. M., Maddala, J. B., & Martin Sagayam, K. (2021). Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network. IETE Journal of Research, 69(5), 2595–2614. https://doi.org/10.1080/03772063.2021.1902868
[4]Balajee, R. M., Mohapatra, H., Deepak, V., et al. (2021). Requirements Identification on Automated Medical Care with Appropriate Machine Learning Techniques. 2021 6th International Conference on Inventive Computation Technologies (ICICT). https://doi.org/10.1109/icict50816.2021.9358683
[5]Bharti, S. K., Varadhaganapathy, S., Gupta, R. K., et al. (2022). Text-Based Emotion Recognition Using Deep Learning Approach. Computational Intelligence and Neuroscience, 2022, 1–8. https://doi.org/10.1155/2022/2645381
[6]Borkar, P., Wankhede, V. A., Mane, D. T., et al. (2023). Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Computing. https://doi.org/10.1007/s00500-023-08615-w
[7]Chauhan, P., Sharma, N., & Sikka, G. (2020). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2601–2627. https://doi.org/10.1007/s12652-020-02423-y
[8]Chintalapudi, K. S., Patan, I. A. K., Sontineni, H. V., et al. (2023). Speech Emotion Recognition Using Deep Learning. 2023 International Conference on Computer Communication and Informatics (ICCCI). https://doi.org/10.1109/iccci56745.2023.10128612
[9]Depuru, S., Nandam, A., Ramesh, P. A., et al. (2022). Sivanantham, Human Emotion Recognition System Using Deep Learning Technique. Journal of Pharmaceutical Negative Results, 13, 4.
[10]Dimlo, U. M. F., Bhanarkar, P., V, J., et al. (2023). Innovative Method for Face Emotion Recognition using Hybrid Deep Neural Networks. 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). https://doi.org/10.1109/icoei56765.2023.10126007
[11]Durga, B. K., & Rajesh, V. (2022). A ResNet deep learning based facial recognition design for future multimedia applications. Computers and Electrical Engineering, 104, 108384. https://doi.org/10.1016/j.compeleceng.2022.108384
[12]Irrinki, M. K. (2021). Learning Through ICT Role of Indian Higher Education Platforms During Pandemic, Library Philosophy and Practice.
[13]Kimmatkar, N. V., & Babu, B. V. (2021). Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques. Computers, 10(3), 37. https://doi.org/10.3390/computers10030037
[14]Kotowski, S. (2020). The semantics of Englishout-prefixation: a corpus-based investigation. English Language and Linguistics, 25(1), 61–89. https://doi.org/10.1017/s1360674319000443
[15]Kuchibhotla, S., Dogga, S. S., Vinay Thota, N. V. S. L. G., et al. (2023). Depression Detection from Speech Emotions using MFCC based Recurrent Neural Network. 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN). https://doi.org/10.1109/vitecon58111.2023.10157779
[16]Kumar, S., Haq, M., Jain, A., et al. (2023). Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance, Computers, Materials and Continua, 74(1), 1523–1540. https://doi.org/10.32604/cmc.2023.028631
[17]Kumar, S., Rani, S., Jain, A., et al. (2022). Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. Sensors, 22(14), 5160. https://doi.org/10.3390/s22145160
[18]Lakshmi, A. J., Kumar, A., Kumar, M. S., et al. (2023). Artificial intelligence in steering the digital transformation of collaborative technical education. The Journal of High Technology Management Research, 34(2), 100467. https://doi.org/10.1016/j.hitech.2023.100467
[19]Madhavi, E., Sivapurapu L., Koppula V., et al. (2023) Developing Learners’ English-Speaking Skills using ICT and AI Tools. Journal of Advanced Research in Applied Sciences and Engineering Technology, 32(2), 142–153. https://doi.org/10.37934/araset.32.2.142153
[20]Mannepalli, K., Sastry, P. N., & Suman, M. (2022). Emotion recognition in speech signals using optimization based multi-SVNN classifier. Journal of King Saud University - Computer and Information Sciences, 34(2), 384–397. https://doi.org/10.1016/j.jksuci.2018.11.012
[21]Mishra, P., & Srinivas, P. V. V. S. (2021). Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage. IAES International Journal of Artificial Intelligence (IJ-AI), 10(4), 889. https://doi.org/10.11591/ijai.v10.i4.pp889-900
[22]Nanduri, V. N. P. S. S., Sagiri, C., Manasa, S. S. S., et al. (2023). A Review of multi-modal speech emotion recognition and various techniques used to solve emotion recognition on speech data. 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA). https://doi.org/10.1109/icirca57980.2023.10220691
[23]Sekhar, C., Rao, M. S., Nayani, A. S. K., Bhattacharyya, D. (2021). Emotion Recognition Through Human Conversation Using Machine Learning Techniques. Advances in Intelligent Systems and Computing, 1280.
[24]Setiawan, R., Devadass, M. M. V., Rajan, R., et al. (2022). IoT Based Virtual E-Learning System for Sustainable Development of Smart Cities. Journal of Grid Computing, 20(3). https://doi.org/10.1007/s10723-022-09616-z
[25]Srinivas, P. V. V. S., & Mishra, P. (2022). A novel framework for facial emotion recognition with noisy and de noisy techniques applied in data pre-processing. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-022-01737-8
[26]Srinivas, P., Khamar, S. N., Borusu, N., et al. (2023). Identification of Facial Emotions in Hitech Modern Era. 2023 2nd International Conference on Edge Computing and Applications (ICECAA). https://doi.org/10.1109/icecaa58104.2023.10212285
[27]Sugumar, R., Sharma, S., Kiran, P. B. N., et al. (2023). Novel Method for Detection of Stress in Employees using Hybrid Deep learning Models. 2023 8th International Conference on Communication and Electronics Systems (ICCES). https://doi.org/10.1109/icces57224.2023.10192609
[28]Thirumuru, R., Gurugubelli, K., & Vuppala, A. K. (2022). Novel feature representation using single frequency filtering and nonlinear energy operator for speech emotion recognition. Digital Signal Processing, 120, 103293. https://doi.org/10.1016/j.dsp.2021.103293
[29]Toirova, G., & Hamroeva, N. (2020). The Importance of Linguistic Models in the Development of Language Bases. Sciences of Europe, (59), 57-63.
[30]Ulugbek, R. (2021). An Analysis of Words Whose Emotional Meaning Changes in Modern English Linguistics. The American Journal of Social Science and Education Innovations, 03(01), 136–142. https://doi.org/10.37547/tajssei/volume03issue01-26
[31]Zhou, C., Li, K., Lu, Y. (2021). Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness. Information Processing & Management, 58(6), 102679. https://doi.org/10.1016/j.ipm.2021.102679
Copyright (c) 2024 Yu Shen
This work is licensed under a Creative Commons Attribution 4.0 International License.
The author(s) warrant that permission to publish the article has not been previously assigned elsewhere.
Author(s) shall retain the copyright of their work and grant the Journal/Publisher right for the first publication with the work simultaneously licensed under:
OA - Creative Commons Attribution License (CC BY 4.0). This license allows for the copying, distribution and transmission of the work, provided the correct attribution of the original creator is stated. Adaptation and remixing are also permitted.
This broad license intends to facilitate free access to, as well as the unrestricted reuse of, original works of all types.
_%E5%89%AF%E6%9C%AC1.png){kind=logo}
