Going Where the Tweets Get Moving! An Explorative Analysis of Tweets Sentiments in the Stock Market

Samuel, Jim; Kretinin, Andrey; Kashyap, Rajiv

doi:10.2139/ssrn.3889567

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Twitter data has been used for a wide range of analyses, including but not limited to healthcare, retail marketing, stock trading, education and politics [17][18][19][20][21][22][23][24]. Twitter data offers a wide range of variables depending on the download programming interface or mechanism used.…”

Section: Twitter Data Analyticsmentioning

confidence: 99%

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

Rahman

Ali

et al. 2020

SSRN Journal

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Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. Statewide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

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Section: Twitter Data Analyticsmentioning

confidence: 99%

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

Rahman

Ali

et al. 2020

SSRN Journal

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“…Sentiment analysis is one of the main research insights benefits from textual analytics as it extracts potentially intended sentiment meaning from the text being analyzed. Early stage past research used custom methods and researcher defined protocols to identify both sentiment and personality traits such as dominance, through the analysis of electronic chat data, and standardized methods to assign positive and negative sentiment scores [14,43,44,45]. Sentiment analysis assigns sentiment scores and classes, by matching keywords and word sequences in the text being analyzed, with prewritten lexicons and corresponding scores or classes.…”

Section: Sentiment Analysismentioning

confidence: 99%

Feeling Like It is Time to Reopen Now? COVID-19 New Normal Scenarios based on Reopening Sentiment Analytics

Samuel,

Rahman,

Ali

et al. 2020

Preprint

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The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences -the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios -an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.

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“…Such exploratory summaries describe the data succinctly, provide a better understanding of the data, and helps generate insights which inform subsequent classification analysis. Past studies have explored custom approaches to identifying constructs such as dominance behavior in electronic chat, indicating the tremendous potential for extending such analyses by using machine learning techniques to accelerate automated sentiment classification and the subsections that follow present key insights gained from literature review to support and inform the Textual Analytics processes used in this study [15][16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…One of the key insights that can be gained from textual analytics is the identification of sentiment associated with the text being analyzed. Extant research has used custom methods to identify temporal sentiment as well as sentiment expressions of character traits such as dominance [19], and standardized methods to assign positive and negative sentiment scores [7,17,53]. Sentiment analysis is broadly described as the assignment of sentiment scores and categories, based on keyword and phrase match with sentiment score dictionaries, and customized lexicons.…”

Section: Sentiment Analyticsmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Samuel,

Ali,

Rahman

et al. 2020

Preprint

Self Cite

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths.We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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Going Where the Tweets Get Moving! An Explorative Analysis of Tweets Sentiments in the Stock Market

Cited by 11 publications

References 10 publications

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

Feeling Like It is Time to Reopen Now? COVID-19 New Normal Scenarios based on Reopening Sentiment Analytics

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

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