Modeling the Geospatial Trend Changes in Jobs and Layoffs by Performing Sentiment Analysis on Twitter Data

doi:10.23940/ijpe.24.02.p7.120130

Abstract

Abstract: Recently, the economy has been hit with a recession that has resulted in increasing job losses and heightened job insecurity. This makes predicting job trends crucial for employers and employees alike. Social media is an innovative technology to mine for sentiment analysis to provide for nuanced and data-driven insights into the current employment status. The proposed work uses VADER, a lexicon and rule-based sentiment analysis tool for calculating sentiment scores for all tweets individually, and classifying them as positive, negative or neutral on the basis of these scores. The primary focus of the work is to 1) Calculate sentiments for tweets and analyse the impact of changes in trajectories in job trends and status of layoffs across various countries and 2) Perform geospatial analysis and machine learning algorithms to represent the current status of layoffs and compare accuracies of various models to highlight the most efficacious one respectively. Three algorithms were used in the study, namely Random Forest, Logistic Regression, and K-Nearest Neighbours, out of which Logistic Regression yielded the highest accuracy of 91.88%.

Key words: recession, layoffs, geospatial, geocoding, machine learning, Twitter

Ronit Bali, Anukansha Sharma, Shuchi Mala, and Yash Malhan. Modeling the Geospatial Trend Changes in Jobs and Layoffs by Performing Sentiment Analysis on Twitter Data [J]. Int J Performability Eng, 2024, 20(2): 120-130.

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References

[1] Villavicencio C., Macrohon J.J., Inbaraj X.A., Jeng J.H., andHsieh J.G.Twitter Sentiment Analysis Towards Covid-19 Vaccines in the Philippines using NaïVe Bayes. Information, vol. 12, no. 5, pp. 204, 2021.
[2] Sharma P., Goyal P., Anubhav K., andAnand E.Sentiment Analysis: Twitter Sentiment Analysis of OTT Platforms in India. Phronimos, vol. 3, no. 1, pp. 16-31, 2023.
[3] Liu X., Shin H., andBurns A.C.Examining the Impact of Luxury Brand's Social Media Marketing on Customer Engagement: using Big Data Analytics and Natural Language Processing. Journal of Business research, vol. 125, pp. 815-826, 2021.
[4] Rustam F., Khalid M., Aslam W., Rupapara V., Mehmood A., andChoi G.S.A Performance Comparison of Supervised Machine Learning Models for Covid-19 Tweets Sentiment Analysis. Plos one, vol. 16, no. 2, pp. e0245909, 2021.
[5] Cyril C.P.D., Beulah, J.R., Subramani, N., Mohan, P., Harshavardhan, A., and Sivabalaselvamani, D. An Automated Learning Model for Sentiment Analysis and Data Classification of Twitter Data using Balanced CA-SVM. Concurrent Engineering, vol. 29, no. 4, pp. 386-395, 2021.
[6] Gopi, A.P., Jyothi, R.N.S., Narayana, V.L., and Sandeep, K.S. Classification of Tweets Data Based on Polarity using Improved RBF Kernel of SVM. International Journal of Information Technology, vol. 15, no. 2, pp. 965-980, 2023.
[7] Imran A.S., Daudpota S.M., Kastrati Z., andBatra R.Cross-Cultural Polarity and Emotion Detection using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets. Ieee Access, vol. 8, pp. 181074-181090, 2020.
[8] Dang N.C.,Moreno-García, M.N., and De la Prieta, F. Sentiment Analysis Based on Deep Learning: A Comparative Study. Electronics, vol. 9, no. 3, pp. 483, 2020.
[9] Basiri M.E., Nemati S., Abdar M., Cambria E., andAcharya U.R.ABCDM: An Attention-Based Bidirectional CNN-RNN Deep Model for Sentiment Analysis. Future Generation Computer Systems, vol. 115, pp. 279-294, 2021.
[10] Ray, P. and Chakrabarti, A.A Mixed Approach of Deep Learning Method and Rule-Based Method to Improve Aspect Level Sentiment Analysis. Applied Computing and Informatics, vol. 18, no. 1/2, pp. 163-178, 2022.
[11] Onan A.Sentiment Analysis on Product Reviews Based on Weighted Word Embeddings and Deep Neural Networks. Concurrency and Computation: Practice and Experience, vol. 33, no. 23, pp. e5909, 2021.
[12] Chakraborty K., Bhatia S., Bhattacharyya S., Platos J., Bag R., andHassanien A.E.Sentiment Analysis of COVID-19 Tweets by Deep Learning Classifiers—A Study to Show How Popularity Is Affecting Accuracy in Social Media. Applied Soft Computing, vol. 97, pp. 106754, 2020.
[13] Boon-Itt, S. and Skunkan, Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health and Surveillance, vol. 6, no. 4, pp. e21978, 2020.
[14] Sailunaz, K. and Alhajj, R.Emotion and Sentiment Analysis from Twitter Text. Journal of Computational Science, vol. 36, pp. 101003, 2019.
[15] Nemes, L. and Kiss, A.Social Media Sentiment Analysis Based on COVID-19. Journal of Information and Telecommunication, vol. 5, no. 1, pp. 1-15, 2021.