[{“box”:0,”content”:”[if 992 equals=”Open Access”]
Open Access
n
[/if 992]n
n
n
n
n
- n t
n
Vaishali Jha, Devendra Kumar Mishra, Kapil Sharma, Ashok Kumar Shrivastava, Samta Jain Goyal
[/foreach]
n
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student, Student, Student, Student, Associate Professor, Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gwalior, Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gwalior, Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gwalior, Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gwalior, Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gwalior, Madhya Pradesh, Madhya Pradesh, Madhya Pradesh, Madhya Pradesh, Madhya Pradesh, India, India, India, India, India
n[/if 1175][/foreach]
[/if 2099][if 2099 equals=”Yes”][/if 2099]nn
Abstract
nThe widespread adoption of social media platforms has transformed the way individuals interact and communicate. Going beyond personal connections, social media has evolved into a potent tool for sharing information, shaping ideas, and fostering participation across various industries. Machine learning is pivotal in enhancing social media’s impact. Social media generates vast data daily, and machine learning is essential for extracting insights. Sentiment analysis, a machine learning application, identifies emotions in social media content, aiding brand tracking and customer insights. Machine learning accurately predicts user behavior on social platforms, enabling tailored content and personalized recommendations, improving user experiences. It’s crucial in social media marketing, to optimize ad campaigns by targeting specific demographics, enhancing engagement, and boosting ROI. In addressing social issues, machine learning detects hate speech, false information, and cyberbullying, creating safer online environments and supporting fact-checking efforts. In governance, it tracks public sentiment and informs policymaking. Challenges include privacy, biases, and ethics. As social media evolves, machine learning must adapt to new data sources.
n
Keywords: Machine learning, social media, Demographics, Proliferation, Sentiment analysis
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Mobile Computing, Communications & Mobile Networks(jomccmn)]
n
n
n
n
n
n
n[if 992 equals=”Open Access”] Full Text PDF Download[else] nvar fieldValue = “[user_role]”;nif (fieldValue == ‘indexingbodies’) {n document.write(‘Full Text PDF‘);n }nelse if (fieldValue == ‘administrator’) { document.write(‘Full Text PDF‘); }nelse if (fieldValue == ‘jomccmn’) { document.write(‘Full Text PDF‘); }n else { document.write(‘ ‘); }n [/if 992] [if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
[1] Du KL, Swamy MN. Neural networks and statistical learning. Springer Science & Business Media; 2013 Dec 9.
[2] Dubey GP, Gupta N, Bhujade RK. A novel approach to intrusion detection system using rough set theory and incremental SVM. International Journal of Soft Computing and Engineering (IJSCE). 2011 Mar;1(1):1448.
[3] Raghuwanshi, A., & Dubey, G. P. (2020). Enhanced PCA-KNN Classifier Method for Effective Diagnosis of a Parkinson’s Disease. International Journal of Emerging Technology and Advanced EngineeringIIJETAE),10(1), 67-72.
[4] Munawar Z, Siswoyo B, Herman NS. Machine learning approach for analysis of social media. ADRI Int. Journal. Information. Technol. 2017 Jan 13;1(1):5-8.
[5] Jang-Jaccard J, Nepal S. A survey of emerging threats in cybersecurity. Journal of computer and system sciences. 2014 Aug 1;80(5):973-93.
[6] Pozzi FA, Fersini E, Messina E, Liu B. Sentiment analysis in social networks. Morgan Kaufmann; 2016 Oct 6.
[7] Xu JM, Burchfiel B, Zhu X, Bellmore A. An examination of regret in bullying tweets. In Proceedings of the 2013 conference of the North American chapter of the Association for Computational Linguistics: Human language technologies 2013 Jun (pp. 697-702).
[8] Dadvar M, Trieschnigg D, Ordelman R, De Jong F. Improving cyberbullying detection with user context. InAdvances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings 35 2013 (pp. 693-696). Springer Berlin Heidelberg.
[9] Dinakar K, Reichart R, Lieberman H. Modeling the detection of textual cyberbullying. InProceedings of the International AAAI Conference on Web and Social Media 2011 (Vol. 5, No. 3, pp. 11-17).
[10] Nahar V, Unankard S, Li X, Pang C. Sentiment analysis for effective detection of cyber bullying. InWeb Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Kunming, China, April 11-13, 2012. Proceedings 14 2012 (pp. 767-774). Springer Berlin Heidelberg.
[11] Yin D, Xue Z, Hong L, Davison BD, Kontostathis A, Edwards L. Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB. 2009 Apr 21;2(0):1-7.
[12] Nahar V, Al-Maskari S, Li X, Pang C. Semi-supervised learning for cyberbullying detection in social networks. InDatabases Theory and Applications: 25th Australasian Database Conference, ADC 2014, Brisbane, QLD, Australia, July 14-16, 2014. Proceedings 25 2014 (pp. 160- 171). Springer International Publishing.
[13] Sivarajah U, Kamal MM, Irani Z, Weerakkody V. Critical analysis of Big Data challenges and analytical methods. Journal of business research. 2017 Jan 1;70:263-86.
[14] Gursoy UT, Bulut D, Yigit C. Social media mining and sentiment analysis for brand management. Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology. 2017;3(1):497-551.
[15] Sharma A, Tahiliani K, Dubey GP. Reactive-Optimized Sentence Detection In Kubernetes Using OpenNLP And Native GraalVM Image With Framework Metric Comparison. In2023 4th International Conference for Emerging Technology (INCET) 2023 May 26 (pp. 1-9). IEEE.
[16] Song M, Park H, Shin KS. Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Information Processing & Management. 2019 May 1;56(3):637-53.
[17] Sanguansat P. Paragraph2vec-based sentiment analysis on social media for business in thailand. In2016 8th International Conference on Knowledge and Smart Technology (KST) 2016 Feb 3 (pp. 175-178). IEEE.
[18] Itani M, Roast C, Al-Khayatt S. Developing resources for sentiment analysis of informal Arabic text in social media. Procedia Computer Science. 2017 Jan 1;117:129-36.
[19] Chekima K, Alfred R. Sentiment analysis of Malay social media text. In Computational Science and Technology: 4th ICCST 2017, Kuala Lumpur, Malaysia, 29–30 November, 2017 2018 (pp. 205-219). Springer Singapore.
[20] Cirqueira D, Pinheiro MF, Jacob A, Lobato F, Santana Á. A literature review in preprocessing for sentiment analysis for Brazilian Portuguese social media. In2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) 2018 Dec 3 (pp. 746-749). IEEE.
[21] Zhang S, Wei Z, Wang Y, Liao T. Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Generation Computer Systems. 2018 Apr 1;81:395-403.
[22] Hamouda A, Rohaim M. Reviews classification using sentiwordnet lexicon. InWorld congress on computer science and information technology 2011 Jan (Vol. 23, pp. 104-105). sn.
[23] El Rahman SA, AlOtaibi FA, AlShehri WA. Sentiment analysis of twitter data. In2019 international conference on computer and information sciences (ICCIS) 2019 Apr 3 (pp. 1-4). IEEE.
[24] Das B, Chakraborty S. An improved text sentiment classification model using TF-IDF and next word negation. arXiv preprint arXiv:1806.06407. 2018 Jun 17..
[25] Khan MT, Durrani M, Ali A, Inayat I, Khalid S, Khan KH. Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling. 2016 Dec;4(1):1-9.
[26] Akter S, Aziz MT. Sentiment analysis on facebook group using lexicon based approach. In2016 3rd international conference on electrical engineering and information communication technology (ICEEICT) 2016 Sep 22 (pp. 1-4). IEEE.
[27] Hassan AU, Hussain J, Hussain M, Sadiq M, Lee S. Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In2017 international conference on information and communication technology convergence (ICTC) 2017 Oct 18 (pp. 138-140). IEEE..
[28] Mahtab SA, Islam N, Rahaman MM. Sentiment analysis on bangladesh cricket with support vector machine. In2018 international conference on Bangla speech and language processing (ICBSLP) 2018 Sep 21 (pp. 1-4). IEEE.
[29] Dhaoui C, Webster CM, Tan LP. Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing. 2017 Sep 11;34(6):480-8.
[30] Peng H, Cambria E, Hussain A. A review of sentiment analysis research in Chinese language. Cognitive Computation. 2017 Aug;9:423-35.
[31] Johnson, L. A. (2020). “Predictive Analytics in Social Media Marketing: A Case Study of Facebook Advertising.” Marketing Science Quarterly, 25(2), 189-207.
[32] GAO Report (2021). Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities. Government Accountability Office. https://www.gao.gov/assets/gao-21-519sp.pdf Accessed on 12-Dec-2023.
[33] Febriana T, Budiarto A. Twitter dataset for hate speech and cyberbullying detection in Indonesian language. In2019 International Conference on Information Management and Technology (ICIMTech) 2019 Aug 19 (Vol. 1, pp. 379-382). IEEE.
[34] Dame Adjin-Tettey T. Combating fake news, disinformation, and misinformation: Experimental evidence for media literacy education. Cogent arts & humanities. 2022 Dec 31;9(1):2037229.
[35] Kaddoura S, Al Husseiny F. The rising trend of Metaverse in education: challenges, opportunities, and ethical considerations. PeerJ Computer Science. 2023 Feb 13;9:e1252.
[36] Dubey GP, Bhujade RK. Investigating the Impact of Feature Reduction Through Information Gain and Correlation on the Performance of Error Back Propagation Based IDS. International Journal of Electrical and Electronics Research (IJEER). 2021;9(3):27-34.
[37] Chen H, Chiang RH, Storey VC. Business intelligence and analytics: From big data to big impact. MIS quarterly. 2012 Dec 1:1165-88.
[38] Himthani P, Dubey GP, Sharma BM, Taneja A. Big data privacy and challenges for machine learning. In2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) 2020 Oct 7 (pp. 707-713). IEEE.
[39] Dubey GP, Bhujade RK, Himthani P. Significance of Dimensionality Reduction in Intrusion Detection Dataset. InInternational Conference on Intelligent Vision and Computing 2021 Oct 3 (pp. 518-529). Cham: Springer International Publishing.
[40] Hashmi SK, Dubey GP, Chourasia V, Yadav A. Generating an Optimal Feature Subset for Performance Enrichment of Intrusion Detection System using GINI. Technology.;5(1):60-70.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
Journal of Mobile Computing, Communications & Mobile Networks
n
n
n
n
n
n
| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | February 14, 2024 | |
| Accepted | March 16, 2024 | |
| Published | April 3, 2024 |
n
n
n
n
n
nn function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n”}]