Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Valle-Cruz, David; Fernandez-Cortez, Vanessa; López-Chau, Asdrúbal; Sandoval‐Almazán, Rodrigo

doi:10.21203/rs.3.rs-39991/v1

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…According to another research, it was discussed that investors' reactions are influenced by information released on Twitter, which has a positive or negative impact on stock indexes, as well as upward or downward trends. Low risk tolerance is the result of negative emotions (Valle-Cruz et al, 2021). There have been many researches carried out to find the correlation between tweets and cryptocurrency price movement after cryptocurrencies emerged mainly due to the fact that the cryptocurrency market is highly volatile and is not centralized and is heavily influenced by external factors.…”

Section: Theoretical Aspect Of the Influence Of Social Network On An ...mentioning

confidence: 99%

Investigating an Individual’s Opinion on Social Media About the Cryptocurrency Market

Rahuf

Maknickienė

2022

12th International Scientific Conference “Business and Management 2022”

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Cryptocurrencies are growing rapidly, with various altcoin being introduced recently, despite the fact that the market is very volatile, cryptocurrency now holds trillions of dollars in the market and has plenty of platforms for trading and owning cryptocurrencies, like Binance, Coinbase, and others. In particular, Bitcoin has caught the atten-tion of many people over the year with a current market cap. of 731.56 billion dollars circulating in the market. One of the major problems in cryptocurrencies is volatility, and often the prices can vary due to the external events that trigger the market. That is, Twitter sentiment. The objective of the article is to investigate people’s opinion about the cryptocurrency market on social media using collected tweets for 2 popular hashtags of Bitcoin and investigating the tweets using sentiment analysis. The study found that sentiment scores could be related to observed price fluctuations.

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Section: Theoretical Aspect Of the Influence Of Social Network On An ...mentioning

confidence: 99%

Investigating an Individual’s Opinion on Social Media About the Cryptocurrency Market

Rahuf

Maknickienė

2022

12th International Scientific Conference “Business and Management 2022”

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“…Other contributions have focused on a Twitter analysis considering several aspects, such as the impact of vaccines on the pandemic [35,36], the impact of COVID on the financial market [37], the use or non-use of masks [38], the effects of confinement [39], and the relationship of COVID with announcements and news [34], among others. In the case of Colombia, some contributions can be found, such as a study [40] where 38,000 Twitter posts on COVID-19 vaccination were analyzed, highlighting opposition to the government with feelings of anger.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture

Miranda

Sánchez-Torres

Salcedo

2023

Data

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The COVID-19 pandemic has had a significant impact on various aspects of society, including economic, health, political, and work-related domains. The pandemic has also caused an emotional effect on individuals, reflected in their opinions and comments on social media platforms, such as Twitter. This study explores the evolution of sentiment in Spanish pandemic tweets through a data analysis based on a fine-tuned BERT architecture. A total of six million tweets were collected using web scraping techniques, and pre-processing was applied to filter and clean the data. The fine-tuned BERT architecture was utilized to perform sentiment analysis, which allowed for a deep-learning approach to sentiment classification. The analysis results were graphically represented based on search criteria, such as “COVID-19” and “coronavirus”. This study reveals sentiment trends, significant concerns, relationship with announced news, public reactions, and information dissemination, among other aspects. These findings provide insight into the emotional impact of the COVID-19 pandemic on individuals and the corresponding impact on social media platforms.

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“…In this way, sentiment analysis has been applied in different health-related contexts [ 29 ]. According to the source of information used, sentiment analysis can be divided into three groups: social networks [ 30 ], especially Twitter, ref. [ 31 ] refereed papers, and texts from interviews with family members [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

Jojoa

Castillo-Sánchez

Garcia‐Zapirain

et al. 2021

IJERPH

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The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

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Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Cited by 11 publications

References 24 publications

Investigating an Individual’s Opinion on Social Media About the Cryptocurrency Market

Investigating an Individual’s Opinion on Social Media About the Cryptocurrency Market

Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

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