Experiences of Alzheimer's disease and related dementia family caregivers on Reddit communities: A topic modeling and sentiment analysis.

Alzheimer's disease and related dementias (ADRD) are a spectrum of disorders characterized by cognitive decline, which pose significant challenges for both affected individuals and their caregivers. Previous literature has focused on patient family surveys which do not always capture the breadth of authentic experiences of the caregiver. Online social media platforms provide a space for individuals to share their experiences and obtain advice toward caring for those with ADRD. This study leverages Reddit, a platform frequented by caregivers seeking advice for caring for a family member with advice for ADRD. To identify the topics of discussion or advice that most caregivers seek and sought after, we employed structured topic modeling techniques such as BERTopic to analyze the content of these posts and use an intertopic distance map to discern the variation in themes across different Reddit categories. In addition, we analyze the sentiment of the Reddit postings using Valence Aware Dictionary and Sentiment Reasoner to deduce the degree of negative, positive, and neutral sentiment of the discussion posts. Our findings reveal that the topics that caregivers most frequently discuss and seek advice for were related to caregiver stories, community support, and concerns ADRD. Specifically, we aimed to reproduce an organic Reddit search of caregiving of abuse on family member, financial struggles, symptoms of hallucinations, and repetition in ADRD family members. These results underscore the importance of online communities for gaining a comprehensive understanding of the multifaceted experiences and challenges faced by ADRD caregivers.

From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.

BACKGROUND: Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. OBJECTIVE: This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. METHODS: We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. RESULTS: A point increase of 1% in negative sentiment rate toward the Asian community in the same month was associated with a 24% increase (incidence rate ratio [IRR] 1.24; 95% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12% decrease (IRR 0.88; 95% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). CONCLUSIONS: A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals.

Peer Support for Chronic Pain in Online Health Communities: Quantitative Study on the Dynamics of Social Interactions in a Chronic Pain Forum.

BACKGROUND: Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain. OBJECTIVE: This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions. METHODS: We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users' language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis. RESULTS: In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with (t198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment (F2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users (ß=-.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure (ß=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users. CONCLUSIONS: Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms.

A Greek Conversational Agent for Hematologic Malignancies: Usability and User Experience Assessment.

Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies. Advancements in artificial intelligence, particularly in areas such as natural language processing and speech recognition, have resulted in the development of interactive tools tailored for healthcare. This paper introduces an innovative conversational agent tailored to the Greek language. The design and deployment of this tool, which incorporates sentiment analysis, aims at gathering detailed family histories and symptom data from individuals diagnosed with hematologic malignancies. Furthermore, we discuss the preliminary findings from a feasibility study assessing the tool's effectiveness. Initial feedback on the user experience suggests a positive reception towards the agent's usability, highlighting its potential to enhance patient engagement in a clinical setting.

Mental health and exercise during the COVID-19 pandemic: A Twitter sentiment analysis.

Beyond its immediate health consequences, the COVID-19 pandemic led to an exacerbation in the mental health of the global population. Regular exercise and its lack thereof are also known to affect mental health. Tweets and their content analysis can provide information about aspects of users' lives including their health habits and mental health. The purpose of this study was to examine individuals' exercise habits and mental health during the pandemic by means of sentiment and correlational analyses. These results indicate that, while exercise and mental health tweets were more COVID-focused in the first 12 months of the pandemic, exercise tweets became more exercise-focused, and mental health tweets became more mental-health-focused eventually during the pandemic. Efforts to increase exercise participation in individuals may prove beneficial. Further research needs to examine the effects of exercise on mental health in the aftermath of COVID-19.

Shopping marketplace analysis based on customer insights using social media analytics.

Using the recent advances in data analytics, Companies can leverage sentiment analysis to identify trends and areas for improvement of their market strategy and operation planning. This analysis allows them to understand the sentiments expressed by customers and accurately predict customer behavior. Social media platforms have fundamentally altered the field of digital marketing. The findings of this research try to provide:•Potential implications for businesses aiming to optimize their product offerings and enhance customer satisfaction within specific cultural contexts.•A case study has been designed for understanding customers' perceptions and satisfaction levels toward various shops, including local ethnic stores and big chain stores.The paper has conducted a literature review around main pillars like application of data analytics on social media, and sales strategies for establishing a marketplace using data analytics. Exploring the utilization of social media and customer feedback, the paper has proposed a conceptual model for creating insights for extraction of shopping experiences and factors used for customer purchasing decision making.

Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challenges.

In recent years, e-commerce platforms have become popular and transformed the way people buy and sell goods. People are rapidly adopting Internet shopping due to the convenience of purchasing from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses increasingly rely on online reviews to assess and improve the quality of products. Existing literature uses natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis and opinion mining, review analysis and management, customer experience and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey also addresses open issues, providing future directions in analyzing online customer reviews.

Investigation of Public Acceptance of Misinformation Correction in Social Media Based on Sentiment Attributions: Infodemiology Study Using Aspect-Based Sentiment Analysis.

BACKGROUND: The proliferation of misinformation on social media is a significant concern due to its frequent occurrence and subsequent adverse social consequences. Effective interventions for and corrections of misinformation have become a focal point of scholarly inquiry. However, exploration of the underlying causes that affect the public acceptance of misinformation correction is still important and not yet sufficient. OBJECTIVE: This study aims to identify the critical attributions that influence public acceptance of misinformation correction by using attribution analysis of aspects of public sentiment, as well as investigate the differences and similarities in public sentiment attributions in different types of misinformation correction. METHODS: A theoretical framework was developed for analysis based on attribution theory, and public sentiment attributions were divided into 6 aspects and 11 dimensions. The correction posts for the 31 screened misinformation events comprised 33,422 Weibo posts, and the corresponding Weibo comments amounted to 370,218. A pretraining model was used to assess public acceptance of misinformation correction from these comments, and the aspect-based sentiment analysis method was used to identify the attributions of public sentiment response. Ultimately, this study revealed the causality between public sentiment attributions and public acceptance of misinformation correction through logistic regression analysis. RESULTS: The findings were as follows: First, public sentiments attributed to external attribution had a greater impact on public acceptance than those attributed to internal attribution. The public associated different aspects with correction depending on the type of misinformation. The accuracy of the correction and the entity responsible for carrying it out had a significant impact on public acceptance of misinformation correction. Second, negative sentiments toward the media significantly increased, and public trust in the media significantly decreased. The collapse of media credibility had a detrimental effect on the actual effectiveness of misinformation correction. Third, there was a significant difference in public attitudes toward the official government and local governments. Public negative sentiments toward local governments were more pronounced. CONCLUSIONS: Our findings imply that public acceptance of misinformation correction requires flexible communication tailored to public sentiment attribution. The media need to rebuild their image and regain public trust. Moreover, the government plays a central role in public acceptance of misinformation correction. Some local governments need to repair trust with the public. Overall, this study offered insights into practical experience and a theoretical foundation for controlling various types of misinformation based on attribution analysis of public sentiment.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

BACKGROUND: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization. OBJECTIVE: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. METHODS: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score. RESULTS: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. CONCLUSIONS: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

Exploring Social Media to Understand Perceptions of Milk Quality among Farmers, Processors, and Citizen-Consumers.

Milk consumption is crucial for a balanced diet, yet recent trends indicate a decline, especially in Italy. A significant factor in this decline is the altered perception of milk quality among consumers, which has created a communication gap between them and other stakeholders. This study aimed to explore the discourse on social media and sentiment towards the concept of milk quality among consumers, farmers, and processors. The research adopted social media analysis to examine online-community messages. A sample of 19,906 Italian comments and posts mentioning keywords "milk", "quality", "cow", and "vaccine" was collected and categorized using term-frequency analysis, correspondence analysis, and sentiment analysis. Results highlighted gaps in perceptions of milk quality: farmers focused on economic issues, consumers on animal welfare and health, and processors on lactose content. For farmers, almost all comments were negative, while for processors, nearly all comments were positive. Consumers presented a more mixed picture. This work contributes to the literature by expanding research on milk quality, using social media as a source of information. The findings suggest that enhancing communication and understanding among these groups could lead to more effective strategies for addressing consumer concerns, potentially reversing the decline in milk consumption.

Examining media's coverage of COVID-19 vaccines and social media sentiments on vaccine manufacturers' stock prices.

Introduction: The COVID-19 pandemic caused a widespread public health and financial crisis. The rapid vaccine development generated extensive discussions in both mainstream and social media, sparking optimism in the global financial markets. This study aims to explore the key themes from mainstream media's coverage of COVID-19 vaccines on Facebook and examine how public interactions and responses on Facebook to mainstream media's posts are associated with daily stock prices and trade volume of major vaccine manufacturers. Methods: We obtained mainstream media's coverage of COVID-19 vaccines and major vaccine manufacturers on Facebook from CrowdTangle, a public insights tool owned and operated by Facebook, as well as the corresponding trade volume and daily closing prices from January 2020 to December 2021. Structural topic modelling was used to analyze social media posts while regression analysis was conducted to determine the impact of Facebook reactions on stock prices and trade volume. Results: 10 diverse topics ranging from vaccine trials and their politicization (note: check that we use American spelling throughout), to stock market discussions were found to evolve over the pandemic. Although Facebook reactions were not consistently associated with vaccine manufacturers' stock prices, 'Haha' and 'Angry' reactions showed the strongest association with stock price fluctuations. In comparison, social media reactions had little observable impact on trading volume. Discussion: Topics generated reflect both actual events during vaccine development as well as its political and economic impact. The topics generated in this study reflect both the actual events surrounding vaccine development and its broader political and economic impact. While we anticipated a stronger correlation, our findings suggest a limited relationship between emotional reactions on Facebook and vaccine manufacturers' stock prices and trading volume. We also discussed potential technical enhancements for future studies, including the integration of large language models.

Prompt engineering with a large language model to assist providers in responding to patient inquiries: a real-time implementation in the electronic health record.

Background: Large language models (LLMs) can assist providers in drafting responses to patient inquiries. We examined a prompt engineering strategy to draft responses for providers in the electronic health record. The aim was to evaluate the change in usability after prompt engineering. Materials and Methods: A pre-post study over 8 months was conducted across 27 providers. The primary outcome was the provider use of LLM-generated messages from Generative Pre-Trained Transformer 4 (GPT-4) in a mixed-effects model, and the secondary outcome was provider sentiment analysis. Results: Of the 7605 messages generated, 17.5% (n = 1327) were used. There was a reduction in negative sentiment with an odds ratio of 0.43 (95% CI, 0.36-0.52), but message use decreased (P < .01). The addition of nurses after the study period led to an increase in message use to 35.8% (P < .01). Discussion: The improvement in sentiment with prompt engineering suggests better content quality, but the initial decrease in usage highlights the need for integration with human factors design. Conclusion: Future studies should explore strategies for optimizing the integration of LLMs into the provider workflow to maximize both usability and effectiveness.

Developing Causality and Severity Assessment Frameworks for Food Safety Signals Using Social Media Reviews: A Technical Report Based on Data From an Urban Indian Suburb.

Social media reviews are a valuable data source, reflecting consumer experiences and interactions with businesses. This study leverages such data to develop a passive surveillance framework for food safety in urban India. By employing a Bidirectional Encoder Representations from Transformers (BERT)-powered Aspect-Based Sentiment Analysis tool, branded as Eat At Right Place (ERP), the study analyses over 100,000 reviews from 93 restaurants to identify and assess food safety signals. The Causality Assessment Index (CAI) and Severity Assessment Score (SAS) are introduced to systematically evaluate potential risks. The CAI uses pattern recognition and temporal relationships to establish causality while the SAS quantifies severity based on sub-aspects such as cleanliness, food handling, and unintended health outcomes. Results indicate that 40% of the restaurants had a CAI above 1, highlighting significant food safety concerns. The framework successfully prioritizes corrective actions by grading the severity of issues, demonstrating its potential for real-time food safety management. This study underscores the importance of integrating innovative data-driven approaches into public health monitoring systems and suggests future improvements in natural language processing algorithms and data source expansion. The findings pave the way for enhanced food safety surveillance and timely regulatory interventions.

Automated sentiment analysis of visually impaired students' audio feedback in virtual learning environments.

This research introduces an innovative intelligent model developed for predicting and analyzing sentiment responses regarding audio feedback from students with visual impairments in a virtual learning environment. Sentiment is divided into five types: high positive, positive, neutral, negative, and high negative. The model sources data from post-COVID-19 outbreak educational platforms (Microsoft Teams) and offers automated evaluation and visualization of audio feedback, which enhances students' performances. It also offers better insight into the sentiment scenarios of e-learning visually impaired students to educators. The sentiment responses from the assessment to point out deficiencies in computer literacy and forecast performance were pretty successful with the support vector machine (SVM) and artificial neural network (ANN) algorithms. The model performed well in predicting student performance using ANN algorithms on structured and unstructured data, especially by the 9th week against unstructured data only. In general, the research findings provide an inclusive policy implication that ought to be followed to provide education to students with a visual impairment and the role of technology in enhancing the learning experience for these students.

Asking questions that are "close to the bone": integrating thematic analysis and natural language processing to explore the experiences of people with traumatic brain injuries engaging with patient-reported outcome measures.

Introduction: Patient-reported outcomes measures (PROMs) are valuable tools for assessing health-related quality of life and treatment effectiveness in individuals with traumatic brain injuries (TBIs). Understanding the experiences of individuals with TBIs in completing PROMs is crucial for improving their utility and relevance in clinical practice. Methods: Sixteen semi-structured interviews were conducted with a sample of individuals with TBIs. The interviews were transcribed verbatim and analysed using Thematic Analysis (TA) and Natural Language Processing (NLP) techniques to identify themes and emotional connotations related to the experiences of completing PROMs. Results: The TA of the data revealed six key themes regarding the experiences of individuals with TBIs in completing PROMs. Participants expressed varying levels of understanding and engagement with PROMs, with factors such as cognitive impairments and communication difficulties influencing their experiences. Additionally, insightful suggestions emerged on the barriers to the completion of PROMs, the factors facilitating it, and the suggestions for improving their contents and delivery methods. The sentiment analyses performed using NLP techniques allowed for the retrieval of the general sentimental and emotional "tones" in the participants' narratives of their experiences with PROMs, which were mainly characterised by low positive sentiment connotations. Although mostly neutral, participants' narratives also revealed the presence of emotions such as fear and, to a lesser extent, anger. The combination of a semantic and sentiment analysis of the experiences of people with TBIs rendered valuable information on the views and emotional responses to different aspects of the PROMs. Discussion: The findings highlighted the complexities involved in administering PROMs to individuals with TBIs and underscored the need for tailored approaches to accommodate their unique challenges. Integrating TA-based and NLP techniques can offer valuable insights into the experiences of individuals with TBIs and enhance the interpretation of qualitative data in this population.

Work-life balance Twitter insights: A social media analysis before and after COVID-19 pandemic.

This research examines the perceptions of Twitter users regarding the prevalent topics within Work-Life Balance communication before and after the COVID-19 pandemic. The pressing questions surrounding current labour market drivers are addressed, particularly regarding the ongoing Fourth Industrial Revolution and the COVID-19 pandemic's impact on communicated themes, particularly in the Human Resource Management field, where Work-Life Balance has emerged as a key concept. Social media platforms like Twitter are pivotal in fostering discussions on Work-Life Balance in society. Over the past decade, Twitter has evolved into a significant research platform researchers utilise in more than ten thousand research articles. The online discourse on Twitter raises awareness of the importance of balancing work and personal life. The COVID-19 pandemic has unveiled new facets of Work-Life Balance, with social media as a key platform for discussing these issues. This research uses Social Media Analysis based on the Hashtag Research framework. A total of 1,768,628 tweets from 499,574 users were examined, and frequency, topic, and sentiment analysis were conducted. Pre-pandemic, the most communicated Work-Life Balance topics were performance and time management, while recruitment and employee development were identified post-pandemic. Pre-pandemic, the highest proportion of negative sentiment was time management and mental health prevention, shifting to time, employee development, and mental health prevention post-pandemic. Despite the limitations of our research, a proposed redefinition of the concept is also presented, including a design for an integrated Work-Life Balance model based on topics communicated by Twitter users. Given the need for a more robust approach to redefining the concept and developing an integrative Work-Life Balance model, the article provides fresh insights for future research.

Whom Would You Help? The Impact of Perpetrator and Victim Gender on Bystander Behavior During a Sexual Assault.

We examined the impact of perpetrator and victim gender on bystander helping choices and assault perceptions. Participants (32 females, 37 males) read about two simultaneously occurring sexual assaults, indicated which victim they would help, and gave their perceptions of the assaults. We used a within-participants design that fully manipulated the perpetrator and victim gender for both assaults. Results showed female victims of male perpetrators and male victims of female perpetrators were most and least likely to be chosen for help, respectively. Cognitive networks derived from open-ended responses provided insight into the rationale used by participants to make helping decisions in ways that differed by perpetrator and victim gender.

MIECF: Multi-faceted information extraction and cross-mixture fusion for multimodal aspect-based sentiment analysis.

Aspect-level sentiment analysis within multimodal contexts, focusing on the precise identification and interpretation of sentiment attitudes linked to the target aspect across diverse data modalities, remains a focal research area that perpetuates the advancement of discourse and innovation in artificial intelligence. However, most existing methods tend to focus on extracting visual features from only one facet, such as face expression, which ignores the value of information from other key facets, such as the textual information presented by the image modality, resulting in information loss. To overcome the aforementioned constraint, we put forth a novel approach designated as Multi-faceted Information Extraction and Cross-mixture Fusion (MIECF) for Multimodal Aspect-based Sentiment Analysis. Our approach captures more comprehensive visual information in the image and integrates these local and global key features from multiple facets. Local features, such as facial expressions and textual features, provide direct and rich emotional cues. By contrast, the global feature often reflects the overall emotional atmosphere and context. To enhance the visual representation, we designed a Cross-mixture Fusion method to integrate this local and global multimodal information. In particular, the method establishes semantic relationships between local and global features to eliminate ambiguity brought by single-facet information and achieve more accurate contextual understanding, providing a richer and more precise manner for sentiment analysis. The experimental findings indicate that our proposed approach achieves a leading level of performance, resulting in an Accuracy of 79.65 % on the Twitter-2015 dataset, and Macro-F1 scores of 75.90 % and 73.11 % for the Twitter-2015 and Twitter-2017 datasets, respectively.

Climate security dialogues on Twitter: An annotated dataset.

Climate security refers to the risks posed by climate change on nations, societies, and individuals, including the possibility of conflicts. As an emerging field of research and public debate, where conceptual definitions are not yet fully agreed upon, gaining insights into global discussions on climate security enables systematizing its various interpretations and framings, mapping thematic priorities, and understanding information gaps that need to be filled. Considering Twitter as an important digital forum for information exchanges and dialogue, the dataset was created through the development of a query strategy based on a snowball scraping technique, which collected tweets containing hashtags related to climate security between January 2014 to May 2023. The dataset comprises 636,379 tweets. Content analysis was performed using text mining and network analysis techniques to generate additional data on sentiment, countries mentioned in the body of tweets, and hashtag co-occurrences. With almost 10 years of data, the utility of this dataset lies in the ability to assess the discursive evolution of a particular topic since its inception.

Experience in psychological counseling supported by artificial intelligence technology.

BACKGROUND: In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. OBJECTIVE: This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. METHODS: Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. RESULTS: The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. CONCLUSION: In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.