Towards a Sentiment Analysis of Tweets from Online Newspapers Regarding the Coronavirus Pandemic

Giulia Pes, Angelica Lo Duca, Andrea Marchetti

Abstract


In the last year, both offline and online news have had the Coronavirus pandemic as their subject, especially since social networking such as Twitter has significantly increased the news regarding Covid-19. The objectives of the project are: the analysis of news regarding the Coronavirus pandemic was extracted from the Twitter profile of ANSA, a well-known Italian news agency, and the analysis of sentiment and the number of likes for each news extracted The sentiment analysis has been carried out using the MAL lexicon (Morphologically Affective Lexicon), where the tweet is split into words and each paola is associated with a score. Positive (with a score greater than zero), negative (with a score less than zero) and neutral (with a score equal to zero) news were identified. As a result, it emerges that sentiment changes day by day, so it is necessary to use sentiment indicators called indices, but only the positive sentiment index is taken into consideration as the negative one is complementary and the neutral one is almost zero. The positive index is then related to some parameters extrapolated from the Civil Protection site: the number of cases, the number of deaths, and the entry into intensive care. Furthermore, in addition to the parameters listed above, the positivity index is related to the days on which the Prime Minister's Decree (DPCM) was signed. The last relationship analyzed is that between the average number of likes and the number of deaths. The results of the research show that the sentiment of the news from the ANSA Agency contains 62.3% of positive news, 37.3% of negative news, and only 0.3% of neutral news. Furthermore, sentiment is not influenced by the daily parameters: the number of cases, number of deaths, entry into intensive care units, and DPCMs. But there is a relationship between the average of like and the number of deaths.

 

Doi: 10.28991/HIJ-2021-02-04-08

Full Text: PDF


Keywords


Coronavirus; Covid-19; Pandemic; SWABS; ANSA; Sentiment Analysis; Civil Protection; DPCM; Italy.

References


Pokharel, B. P. (2020). Twitter Sentiment Analysis during Covid-19 Outbreak in Nepal. SSRN Electronic Journal. doi:10.2139/ssrn.3624719.

Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends (Preprint). doi:10.2196/preprints.19447.

Imran, A. S., Daudpota, S. M., Kastrati, Z., & Batra, R. (2020). Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets. IEEE Access, 8, 181074–181090. doi:10.1109/access.2020.3027350.

Rajput, N. K., Grover, B. A., & Rathi, V. K. (2020). Word frequency and sentiment analysis of twitter messages during coronavirus pandemic. arXiv preprint arXiv:2004.03925.

Pes G., Lo Duca A., Marchetti A., (2020), Tweetpy: a software to extract tweets related to a single Twitter user. "CNR", 1-11.

Wiberg, M. (2005). The Interaction Society: Practice, Theories and Supportive Technologies. IGI Global Publishing, Pennsylvania, United States

Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. Proceedings of the Workshop on Languages in Social Media, LSM’11, Oregon, United States.

Balahur, A., & Steinberger, R. (2009). Rethinking Sentiment Analysis in the News: from Theory to Practice and back. Proceeding of WOMSA, 1-12.

Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-Scale Sentiment Analysis for News and Blogs. ICWSM, 7(21), 219-222, Colorado, United States.

Chakraborty, A., & Bose, S. (2020). Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment. Journal of Computational Social Science, 3(2), 367–400. doi:10.1007/s42001-020-00088-3.

Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, 1(12), Stanford, California, United States.

Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., Van Der Goot, E., Halkia, M., ... & Belyaeva, J. (2013). Sentiment analysis in the news. arXiv preprint arXiv:1309.6202.

Chen, H., Zhu, Z., Qi, F., Ye, Y., Liu, Z., Sun, M., & Jin, J. (2021). Country Image in COVID-19 Pandemic: A Case Study of China. IEEE Transactions on Big Data, 7(1), 81–92. doi:10.1109/tbdata.2020.3023459.

Kruspe, A., Häberle, M., Kuhn, I., & Zhu, X. X. (2020). Cross-language sentiment analysis of european twitter messages duringthe covid-19 pandemic. arXiv preprint arXiv:2008.12172.

Pran, M. S. A., Bhuiyan, M. R., Hossain, S. A., & Abujar, S. (2020, July). Analysis of Bangladeshi People's Emotion during Covid-19 In Social Media Using Deep Learning. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-6. IEEE. doi:10.1109/ICCCNT49239.2020.9225500.

Kim, B. (2020). Effects of social grooming on incivility in COVID-19. In Cyberpsychology, Behavior, and Social Networking 23(8), 519-525. doi:10.1089/cyber.2020.0201.

Ho, Y. F. W., Hiu, Y. S. L., Ambrose, H. T. F., Siu, T. L., Thomas, W. Y. C., Christine, S. Y. L., ... & Ming-Yen, N. (2020). Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology, 201160.

Padmaja, S., S, P., & Bandu, S. (2014). Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles. International Journal of Advanced Research in Artificial Intelligence, 3(11), 1–58. doi:10.14569/ijarai.2014.031101.

Nurlaila, I., Rahutomo, R., Purwandari, K., & Pardamean, B. (2020). Provoking Tweets by Indonesia Media Twitter in the Initial Month of Coronavirus Disease Hit. 2020 International Conference on Information Management and Technology (ICIMTech). doi:10.1109/icimtech50083.2020.9211179.

Chun, S. A., Li, A. C.-Y., Toliyat, A., & Geller, J. (2020). Tracking Citizen’s Concerns during COVID-19 Pandemic. The 21st Annual International Conference on Digital Government Research. Digitranscope Spring Institute, Seoul National University, Seoul, Korea. doi:10.1145/3396956.3397000.

Marco, V., Giuliano, G., Basile, V., & Bosco, C. (2019). The Tenuousness of lemmatization in lexicon-based sentiment analysis. In Sixth Italian Conference on Computational Linguistics, 2481, 1-6.

Ferro, M., Pezzulo, G., & Pirrelli, V. (2010). Morphology, memory and the mental lexicon. Lingue e Linguaggio, 9(2), 199-0.

Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Association for Computational Linguistics, University of Pennsylvania, Philadelphia, United States.


Full Text: PDF

DOI: 10.28991/HIJ-2021-02-04-08

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Giulia Pes, Angelica Lo Duca, Andrea Marchetti