Advancements in NLP for social media analytics

Global health emergencies, such as the COVID-19 pandemic, have demonstrated the power and volatility of social media as a platform for public communication. Millions of people worldwide relied on digital platforms like Twitter and Facebook for real-time information, updates, and expressions of emotion. However, this rapid flow of information also led to the widespread diffusion of misinformation and changing patterns of discourse; a phenomenon that calls for a systematic investigation. This study explores topic evolution in social media discourse during global health emergencies using Natural Language Processing methods, including topic modelling (LDA and BERTopic), sentiment analysis (VADER and BERTweet), and geospatial trend analysis. A dataset of 70,452 COVID-19-related tweets collected from Nigeria between March 2020 and December 2022 was analyzed using Natural Language Processing (NLP) and data mining techniques. Topic modelling (Latent Dirichlet Allocation and BERTopic), sentiment analysis (VADER and BERTweet), and geospatial trend mapping were employed to examine the interplay between sentiment and thematic focus over time. The results reveal five dominant thematic clusters— public health awareness , lockdown and economy , vaccine and misinformation , recovery and hope , and political accountability . Temporal and spatial analyses further indicate that public sentiment and attention evolved in alignment with pandemic milestones such as lockdown enforcement, vaccine rollout, and policy controversies. The study contributes a hybrid analytical framework that integrates sentiment and topic evolution modelling for infodemic monitoring. By mapping discourse evolution across phases of a crisis, the research provides actionable insights for policymakers, communication strategists, and public health agencies to design adaptive and evidence-driven responses during global health emergencies.

Public discourse and sentiment during Mpox outbreak: an analysis using natural language processing

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

Natural language processing (NLP) has been changed by neural networks, which make it possible to analyze social media data in more detail, even though it is very large, not organized, and changes all the time. This abstract looks at how neural networks can be used in natural language processing (NLP) to analyze social media. Every day, social media sites produce huge amounts of written content that covers a wide range of topics, feelings, and writing styles. The subtleties and complexity of this data are often too much for traditional NLP methods to handle. Neural networks, on the other hand, offer strong answers because they can learn complex patterns and models from raw text. Sentiment analysis is one of the main ways that neural networks are used in social media research. Deep learning designs like recurrent neural networks (RNNs) or more advanced models like transformer-based architectures (e.g., BERT, GPT) can correctly describe how people feel about social media posts. Businesses can use this feature to find out what the public thinks, keep track of how people feel about their brand, and spot new trends in real time. Neural networks are useful for more than just analyzing mood. They can also help with named entity recognition (NER), topic modeling, and even figuring out humor and irony in text, which is hard for traditional rule-based systems because they rely on set rules and definitions. Also, neural networks are great at processing data in more than one language, which is important because social media is used all over the world. When learned on big datasets, models can be used across languages, giving us information about different language groups without a lot of human tweaking. Another big benefit of neural network methods is that they can be scaled up or down, which is very important for handling the huge amounts of data that social media sites create.

Research on lifestyle changes during the COVID-19 pandemic often relies on Likert-type scale question surveys. Survey participants respond to questions by selecting one of the numerically ordered choices, "Strongly Disagree"=1, "Disagree"=2, "Neutral"=3, "Agree"=4, and "Strongly Agree"=5. Analyzing Likert-type data requires statistical methods beyond approaches like linear regression. First, it is unclear if the distance between choices are truly equal. For example, are "Agree" and "Strongly Agree" more close than "Neutral" and "Agree? Second, summarizing results using traditional means makes little sense. For example, would a mean of 4.5 imply "Agree and a half"? Finally, participants tend to select more central choices and less extremes.

In an era where brand perception can be reshaped within minutes on digital platforms, organizations face immense reputational risks during crises. Whether due to product failures, executive scandals, data breaches, or socio-political missteps, brand crises elicit public backlash that unfolds rapidly across social media, news outlets, and consumer forums. Monitoring and restoring brand trust under such volatile conditions demands tools capable of real-time, context-sensitive, and nuanced analysis of public sentiment. This paper explores the role of advanced sentiment analysis models in crisis-time brand trust monitoring and recovery. Drawing on recent advancements in natural language processing (NLP), deep learning, and emotion-aware AI, we examine how modern models—such as transformer-based architectures (e.g., BERT, RoBERTa), hybrid rule-based-deep learning systems, and affective computing algorithms—outperform traditional lexicon and statistical techniques in capturing the subtleties of sentiment, emotion, and trust dynamics during brand crises. We develop a conceptual framework that integrates sentiment analytics with crisis communication strategies and outline its application through cross-sector scenarios. Through a systematic literature review, we highlight challenges in multilingual processing, sarcasm detection, temporal sentiment tracking, and model explainability. The paper concludes with recommendations for deploying sentiment analysis tools responsibly, ensuring ethical AI governance, and aligning model outputs with actionable recovery strategies for brand managers.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

BackgroundTeaching and learning about topics such as bias are challenging due to the emotional nature of bias-related discourse. However, emotions can be challenging to study in health professions education for numerous reasons. With the emergence of machine learning and natural language processing, sentiment analysis (SA) has the potential to bridge the gap.ObjectiveTo improve our understanding of the role of emotions in bias-related discourse, we developed and conducted a SA of bias-related discourse among health professionals.MethodsWe conducted a 2-stage quasi-experimental study. First, we developed a SA (algorithm) within an existing archive of interviews with health professionals about bias. SA refers to a mechanism of analysis that evaluates the sentiment of textual data by assigning scores to textual components and calculating and assigning a sentiment value to the text. Next, we applied our SA algorithm to an archive of social media discourse on Twitter that contained equity-related hashtags to compare sentiment among health professionals and the general population.ResultsWhen tested on the initial archive, our SA algorithm was highly accurate compared to human scoring of sentiment. An analysis of bias-related social media discourse demonstrated that health professional tweets (n=555) were less neutral than the general population (n=6680) when discussing social issues on professionally associated accounts (χ2 [2, n=555)]=35.455; P<.001), suggesting that health professionals attach more sentiment to their posts on Twitter than seen in the general population.ConclusionsThe finding that health professionals are more likely to show and convey emotions regarding equity-related issues on social media has implications for teaching and learning about sensitive topics related to health professions education. Such emotions must therefore be considered in the design, delivery, and evaluation of equity and bias-related education.

Natural Language Processing (NLP) plays a critical role in the development of chatbots, enabling them to understand and generate human-like language. This paper provides a comprehensive review of the applications, challenges, and future directions of NLP in chatbots. It discusses the fundamental principles of NLP, including tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis, and examines how these techniques are used in chatbots. The paper also explores the challenges and limitations of NLP in chatbots, such as ambiguity in language, multilingual support, privacy concerns, and integration with existing systems. Additionally, it discusses recent advances in NLP, such as neural language models and transfer learning, and their potential impact on the future development of chatbots. Ethical considerations in NLP development are also addressed. Overall, the paper highlights the significant role of NLP in advancing chatbot technology and the challenges that must be overcome to realize its full potential.

There is a rich collection of studies exploring different aspects of consumer brand relationship. Traditional approaches of questionnaires and analysis are based on measurements collected from a relatively small number of survey participants. With the advancements in natural language processing (NLP) techniques, opportunities exist for applying NLP techniques to discover consumer brand relationship from social media platforms that possess a large amount of data on consumer opinion and sentiment. In this study, we review consumer brand relationship analysis focusing on leveraging NLP and machine learning techniques to address some challenges associated with discovering customer brand relationship from social media data and propose a methodological framework for the approach. This study has implications for both academic research and practitioners as it presents an alternative way to investigate consumer brand relationship.KeywordsNLPText miningConsumer brand relationshipMachine learning

Abstract—Sentiment analysis is an important part of natural language processing (NLP) and has many applications in social media, e-commerce, and other fields. This study aims to provide a clear distinction between these methodologies by offering a structured overview of sentiment analysis and the variety of techniques used in its execution. The article examines different machine learning algorithms for sentiment analysis and high- lights their advantages and disadvantages by drawing on credible prior research on the subject. Additionally, the study provides a tabular comparison of different machine learning methods by selecting suitable parameters. Keywords: Decision Tree, Support Vector Machine (SVM), Random Forest, Convolution neural network (CNN), Neural network, Long Short-Term Memory Networks (LSTM), BERT

Previously, when looking towards the development of surveys to help us improve aftershave products, we relied on the traditional and often costly means of conducting comprehensive and expensive marketing surveys. Social media plays the role of a powerful tool for realizing consumer behavior and obtaining opinion of consumers of products that is referred to as consumer sentiment analysis. It observes and checks consumer preferences by investigating the content they are sharing, where they can be any images, videos, or simple text. Catchers get a new insight into target market needs and service adequacy. By tailoring to the consumers' needs as these demands keep changing, companies can make their operations better. Social media handling companies can gain rich insights from social media applications by using latest technologies like machine learning and natural language processing, especially through lexicon-based methods. Then, they can randomly select a few customers and conduct sentiment analysis on their feedback of the product which is known as opinion mining.

BackgroundLoneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon. ObjectivesThis paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed. MethodsThis study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations. ResultsThe analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue. ConclusionThis paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.

A Review on Reddit News Headlines with NLTK tool

Ozempic (Glucagon-like peptide 1 receptor agonist) in social media posts: Unveiling user perspectives through Reddit topic modeling