Sentiment Analysis on COVID-19 Twitter Data

Vijay, T.; Chawla, Ayan; Dhanka, Balan; Karmakar, Purnendu

doi:10.1109/icraie51050.2020.9358301

Cited by 38 publications

(26 citation statements)

References 2 publications

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“…Klasifikasi teks dari postingan media sosial [5], [6] selalu menjadi masalah penelitian yang menarik dan memiliki tantangan tertentu. Kajian analisis sentimen media sosial tentang COVID-19 menghasilkan lima tema relevan yang berkisar dari positif hingga negatif [7]- [11].…”

Section: Pendahuluanunclassified

Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

Lestandy

Abdurrahim

Syafaah

2021

RESTI

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COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.

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Section: Pendahuluanunclassified

Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

Lestandy

Abdurrahim

Syafaah

2021

RESTI

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“…Existing studies on COVID Twitter sentiment analysis (Manguri et al, 2020;Kaur and Sharma, 2020;Vijay et al, 2020;Chakraborty et al, 2020;Singh et al, 2021) mostly use TextBlob (Loria, 2018), or some simple supervised models (Machuca et al, 2021;Kaur et al, 2021;Mansoor et al, 2020).…”

Section: Classify Sentiments Towards Governorsmentioning

confidence: 99%

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States

Jin¹,

Peng²,

Vaidhya³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Mining the causes of political decisionmaking is an active research area in the field of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on surveys as the main source of predictors. However, the recent COVID-19 pandemic has given rise to a new political phenomenon, where political decisionmaking consists of frequent short-term decisions, all on the same controlled topic-the pandemic. In this paper, we focus on the question of how public opinion influences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and comparisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.

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“…Kudchadkar et al [24] observed trends in concurrent #PedsICU and #COVID19 usage which reflect evolving information, knowledge gained, and collaborations among the global pediatric critical care community in Twitter. In terms of sentiment analysis, Vijay et al [25] analysed the tweets regarding COVID-19 from November 2019 to May 2020 in India and its effect. Most people started having Negative tweets but with increasing time shifted towards positive and neutral comments.…”

Section: B Content Analysis Reviewmentioning

confidence: 99%

Dataset of Coronavirus Content From Instagram With an Exploratory Analysis

et al. 2021

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The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professionals' engagement in social media. Online Social Networks are playing an important role in news propagation as well as keeping people in contact. At the same time, social media is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this study, we publish a multilingual coronavirus (COVID-19) Instagram dataset that we have continuously collected during the first wave of the pandemic from 5 January 2020 to 30 May 2020. The dataset contains 25.7K posts, 829K comments, and 3.2M likes in various subjects from different publishers such as 'public accounts', 'fake accounts (bots)', 'newsagencies', 'influencers', 'celebrities', 'business pages', etc. In addition to the dataset, this paper provides an analysis of the behaviour of the publishers. We study the behavioural aspects of the users in terms of their engagement, use of hashtags, activities, reactions as well as a full analysis of the published content related to the COVID-19. We believe this contribution helps the research community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media.

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Sentiment Analysis on COVID-19 Twitter Data

Cited by 38 publications

References 2 publications

Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States

Dataset of Coronavirus Content From Instagram With an Exploratory Analysis

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