Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts (KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.

Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon - we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages. hor[RU,RA]{\large Richard Anderson} \address[RU]{\large Rutgers University, USA} \address[RA]{\large rick.anderson@rutgers.edu} \author[RU]{\large Carmela Scala} \author[RU]{\large Jim Samuel} \author[UC]{\large Vivek Kumar} \address[UC]{\large University of Cagliari, Italy} \author[RU]{\large Parth Jain}

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.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled 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 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 Naive 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.

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.