Harnessing the power of ML and NLP for decision making in education sector from social media data
Автор: Murthy H., Lamkuche H.
Журнал: Cardiometry @cardiometry
Рубрика: Original research
Статья в выпуске: 22, 2022 года.
Бесплатный доступ
The active social media users across the globe have passed the 3.8 billion mark. Platforms like Facebook, Reddit, Instagram, Twitter, and more are an ocean full of opinions and views. With over 500 million tweets being generated daily, this enormous volume of data can offer very prominent insights and allow organizations and businesses to make strategic decisions. The COVID-19 pandemic has changed the landscape of learning and education dramatically. With a sudden deviation from the classroom in many parts of the world, some wonder if the adoption of online learning will continue with the outbreak of the post-epidemic epidemic and how such a change could affect the global education market. The crux of the problem is how we need to analyze vast amounts of data efficiently. We chose to employ advanced ML and NLP techniques to analyze the sentiment of the masses on digital learning and important extracting demographic information from them. In this paper, we will make an effort to understand the orientation of the academicians towards the recent online education adoption. We will collect the data from the tweets using the trending tags of COVID-19 and Online classes.
Social media, tweets, pandemic, nlp, covid-19, sentiment analysis, education
Короткий адрес: https://sciup.org/148324623
IDR: 148324623 | DOI: 10.18137/cardiometry.2022.22.415420
Список литературы Harnessing the power of ML and NLP for decision making in education sector from social media data
- Pak, P . Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining, Proceedings of the Seventh Conference on International Language Resources and Evaluation, (2010)
- Bifet, E. Frank, Sentiment Knowledge Discovery in Twitter Streaming Data,” Proceedings of the 13th International Conference on Discovery Science, Berlin, German, (2010)
- Chengang Zhu, Guang Cheng (senior member, IEEE) and Kun Wang (senior member, IEEE) Big data analytics for program popularity prediction in broadcast TV industries, (2017)
- AlecGo, R. Bhayani, L. Huang, Twitter Sentiment Classification Using Distant Supervision, Stanford University, Technical Paper, (2009)
- Irena Pletikosa Cvijikj, Florian Michahelles, Monitoring Trends on Facebook, Ninth IEEE International Conference on Dependable, Autonomic and Secure Computing, (2011)
- Jim Samuel 1, G. G. Md. Nawaz Ali 2, Md. Mokhlesur Rahman 3, EkEsawi 4 and Yana Samuel 5 COVID-19 Public Sentiment Insights and Machine Learning for Tweets, (2020)
- Mohammad Noor Injadat, FadiSalo, Ali Bou Nassif, Data mining techniques in social media: A survey, Neurocomputing- ScienceDirect, (2016)
- M.S, Neethu, R. Rajashree, Sentiment Analysis in Twitter using Machine Learning Techniques, IEEE, Tiruchengode, (2013)
- Noviyanti. P(1), Fra siskus Dian Arianto(2), Social Media Analysis: Utilization of Social Media Data for research on COVID-19, (2020)
- P. Noviyanti, Fra siskus Dian Arianto Social Media Analysis: Utilization of Social Media Data for research on COVID-19, (2020)
- Pablo Gamallo, Marcos Garcia Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets, 8th International Workshop on Semantic Evaluation, Dublin, (2014)
- J.V. Praneeth Sai, Bhuvaneswari Balachander, Sentimental Analysis of Twitter Data Using Tweepy and Textblob, International Journal of Advanced Science and Technology, (2020)
- Prashant Sahatiya Big Data Analytics on Social Media Data: A Literature Review, International Research Journal of Engineering and Technology, Gujarat, India, (2018)
- Raghava Rao Mukkamala, Jannie Iskou Sørensen, Abid Hussain and Ravi Vatrapu, Social Set Analysis of Corporate Social Media Crises on Facebook, IEEE EDOC 2015, South Australia, (2015)
- Saeideh Shahheidari, Hai Dong, and Md Nor Ridzuan Bin Daud , Twitter sentiment mining: A multi-domain analysis, CISIS Seventh International Conference IEEE, (2013)
- A. Vishal, S.S. Kharde, Sonawane, Sentiment Analysis of Twitter Data: A Survey of Techniques,” International Journal of Computer Applications,139, 0975 – 8887, (2016)
- Zhunchen Luo, Miles Osborne, Ting Wang, An effective approach to tweets opinion retrieval, Springer Journal on WorldWideWeb, (2013)
