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Information Journal Paper

Title

Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network

Pages

  109-134

Abstract

 With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about the Covid-19 epidemic in Iran. This study analyzes the emotions in the opinions of the Iranian people on the social network Twitter during the Corona crisis. For this purpose, a Deep Neural Network model is presented. As there is no labeled dataset of Covid-19 tweets, the proposed model is first trained on the Stanford University Sentiment140 dataset, which contains 1. 6 million tweets, and then used to classify the two classes of emotions contained in the collected corona-related tweets in Iran. The results show that the percentage of tweets with negative emotions is significantly higher than positive tweets. Also, the change in negative emotions of people in different months is proportional to the change in patient statistics.

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    APA: Copy

    Basiri, Mohammad Ehsan, Habibi, Shirin, & Nemati, Shahla. (2021). Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network. JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES, 9(37 ), 109-134. SID. https://sid.ir/paper/1030700/en

    Vancouver: Copy

    Basiri Mohammad Ehsan, Habibi Shirin, Nemati Shahla. Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network. JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES[Internet]. 2021;9(37 ):109-134. Available from: https://sid.ir/paper/1030700/en

    IEEE: Copy

    Mohammad Ehsan Basiri, Shirin Habibi, and Shahla Nemati, “Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network,” JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES, vol. 9, no. 37 , pp. 109–134, 2021, [Online]. Available: https://sid.ir/paper/1030700/en

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