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Biblioqrafik təsvir | Hajirahimova , M.S. Machine learning-based sentiment analysis of Twitter data / M.S. Hajirahimova , M.I. Ismayilova // Problems of information society. - 2022. - N: no.1, vol.13. - P. 52–60. | Annotasiya | The paper analyzes the views of Twitter users on the COVID-19 corona virus pandemic based
on machine learning algorithms. The role of sentiment analysis increased with the advent of the
social network era and the rapid spread of microblogging applications and forums. Social networks
are the main sources for gathering information about users’ thoughts on various themes. People
spend more time on social media to share their thoughts with others. One of the themes discussed
on social networking platforms Twitter is the COVID-19 corona virus pandemic. In the paper,
machine learning methods as Naive Bayes, Support Vector Machine, Random Forest, Neural
Network are used to analyze the emotional “color” (positive, negative, and neutral) of tweets
related to the COVID-19 corona virus pandemic. The experiments are conducted in Python
programming using the scikit-learn library. A tweet database related to the COVID-19 corona virus
pandemic from the Kaggle website is used for experiments. The RF classifier shows the highest
performance in the experiments. | Elektron variant | Elektron variant |
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