Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

Chintalapudi, Nalini; Battineni, Gopi; Amenta, Francesco

doi:10.3390/idr13020032

Cited by 131 publications

(78 citation statements)

References 36 publications

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“…Recently, studies have been reporting applications of BERT-like models for sentiment analysis in the domain of COVID-19. Chintalapudi et al [51] described the sentiment analysis of a COVID-19 dataset in the Indian language collected from Twitter between 23 March 2020 and 15 July 2020. They used BERT model, and compared it with three other models, namely logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM).…”

Section: Bert-based Language Modelsmentioning

confidence: 99%

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

et al. 2021

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This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian language. We collected and analysed 206,196 tweets related to COVID-19 and constructed a dataset of 10,000 tweets which we manually annotated with a sentiment label. We trained the Cro-CoV-cseBERT language model for the representation and clustering of tweets. Additionally, we compared the performance of four machine learning algorithms on the task of sentiment classification. After identifying the best performing setup of NLP methods, we applied the proposed framework in the task of characterisation of COVID-19 tweets in Croatia. More precisely, we performed sentiment analysis and tracked the sentiment over time. Furthermore, we detected how tweets are grouped into clusters with similar themes across three pandemic waves. Additionally, we characterised the tweets by analysing the distribution of sentiment polarity (in each thematic cluster and over time) and the number of retweets (in each thematic cluster and sentiment class). These results could be useful for additional research and interpretation in the domains of sociology, psychology or other sciences, as well as for the authorities, who could use them to address crisis communication problems.

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Section: Bert-based Language Modelsmentioning

confidence: 99%

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

et al. 2021

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“…The transformer models (such as BERT) are based on a deep neural network architecture with a self-attention mechanism for language understanding. Such models have shown performance improvement in classification tasks of social media text (Naseem et al 2020;Jiang et al 2019), most notably analyzing sentiment related to COVID-19 pandemic (Ghasiya and Okamura 2021;Singh et al 2021;Chintalapudi et al 2021). Due to the limitations of the computing environment, we did not include transformer-based models in this study.…”

Section: Discussionmentioning

confidence: 99%

Text Mining for U.S. Pension De-Risking Analysis

Zhang

Tian

2022

Risks

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In the past 30 years, as sponsors of defined benefit (DB) pension plans were facing more severe underfunding challenges, pension de-risking strategies have become prevalent for firms with DB plans to reduce pension-related risks. However, it remains unclear how pension de-risking activities affect firms’ performance, partially due to the lack of de-risking data. In this study, we develop a multi-phase methodology to build a de-risking database for the purpose of investigating impacts of firms’ pension risk transfer activities. We extract company filings between 1993 and 2018 from the SEC EDGAR database to identify different “de-risking” strategies that US-based companies have used. A combination of text mining, machine learning, and natural language processing methods is applied to the textual data for automated identification and classification of de-risking strategies. The contribution of this study is three-fold: (1) the design of a multi-phase methodology that identifies and extracts hidden information from a large amount of textual data; (2) the development of a comprehensive database for pension de-risking activities of US-based companies; and (3) valuable insights to companies with DB plans, pensioners, and practitioners in pension de-risking markets through empirical analysis.

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“…[20] used supervised K-Nearest Neighbor (KNN) technique to classify the sentiments of people about COVID-19 vaccination. [21] collected Indian people's tweets from twitter websites during the time period of 23rd March 2020 to 15th July 2020 for sentiment analysis. Initially, the collected data were labelled as anger, joy, fear, and sad.…”

Section: Related Workmentioning

confidence: 99%