Analyzing User Comments on Covid-19 Pandemic with Word2Vec Technique

Yıl 2021, Cilt: 6 Sayı: 1, 119 - 129, 31.05.2021

Nimet Aksoy , Özlem Tülek , Özlem Aydın Erol Özçekiç

Öz

In Covid-19 pandemic, people spend more time at home than before the pandemic. Due to this reason, more time is spent on the internet than before. People expressed their views and assessments about Covid-19 pandemic on social media. Within the scope of this study, we collected people’s comments on different topics about Covid-19 pandemic on the internet and we evaluated them using Word2Vec technique. With this technique, vectors of words in a document are calculated and the semantic relationship between words is captured. The collected data include March and April data, so we compared the results of the two months. As a result of this study, many different results were found about people’s views and opinions about the pandemic. The results of this study can be used in the future as automatic psychological evaluation studies with natural language processing techniques. And the trained model will be shared on internet platforms.

Anahtar Kelimeler

Word Embedding, Word2Vec, NLP, Covid-19

Kaynakça

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• Mikolov, T., Kai, C., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings.
• Mikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting similarities among languages for machine translation. arXive:1309.4168.
• Naili, M., Habacha, A., & Ghezala, H. B. (2017). Comparative study of word embedding methods in topic segmentation. Procedia Computer Science, 112, 340-349.
• Tripathy, A., Agrawal, A., & Rath, S. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57(October 2017), 117-126.
• Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., & Shah, Z. (2020). Top concerns of tweeters during the COVID-19 pandemic: infoveillance study. Journal of Medical Internet Research, 22(4).
• Aggarwal, C., & Zhai, C. (2013). Mining text data.
• Dasri, Y. B., Barde, B. V., Shivajirao, N. P., & Bainwad, M. A. (2017). Text mining framework, methods and techniques. IOSR Journal of Computer Engineering, 19(4), 19-22.
• Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. Proceedings of 2014 Science and Information Conference, SAI 2014.
• Li, Y., & Yang, T. (2018). Word embedding for understanding natural language: a survey. Guide to big data applications (s. 83-104). içinde Springer International Publishing.
• Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., & Joulin, A. (2018). Advances in pre-training distributed word representations. LREC 2018 - 11th International Conference on Language Resources and Evaluation. Miyazaki, Japan.
• Mikolov, T., Kai, C., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings.
• Mikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting similarities among languages for machine translation. arXive:1309.4168. Naili, M., Habacha, A., & Ghezala, H. B. (2017). Comparative study of word embedding methods in topic segmentation. Procedia Computer Science, 112, 340-349.
• Tripathy, A., Agrawal, A., & Rath, S. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57(October 2017), 117-126.
• Wiederhold, B. K. (2020). Using social media to our advantage: alleviating anxiety during a pandemic. Cyberpsychology, Behavior and Social Networking, 23(4).