Asymmetric impacts of climate policy uncertainty, investor sentiment on energy prices and renewable energy consumption: Evidence from NARDL and wavelet coherence.

This paper seeks to look into the asymmetric impacts posed by climate policy uncertainty (CPU) and investor sentiment (IS) upon the price of non-renewable energy, specifically natural gas prices, and the consumption of renewable energy, embodied in geothermal energy, biofuels, and fuel ethanol. To this end, the analysis draws on a non-linear autoregressive distributed lag (NARDL) model and wavelet coherence (WTC) technique with monthly data from January 2000 to December 2021. The NARDL results establish an asymmetric association between the variables, where negative shocks to CPU exert a greater effect on each energy variable than positive shocks, while the reverse is true for IS. Furthermore, it has been noticed that CPU and IS exhibit primarily negative correlations with the target variables over the long term, with CPU having a more pronounced effect on natural gas prices than on other forms of renewable energy consumption. Wavelet analysis also reveals that CPU leads the energy variables over the medium to long run, while IS assumes a dominant role in the short to medium run. These momentous findings underscore the importance of this study in informing energy policy formulation and environmental management, as well as optimizing investor portfolios.

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.

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

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.

Public Perceptions of the Australian Health System During COVID-19: Findings From a 2021 Survey Compared to Four Previous Surveys.

BACKGROUND: This study examines the perceptions of the Australian public canvassed in 2021 during the COVID-19 pandemic about their health system compared to four previous surveys (2008, 2010, 2012 and 2018). METHODS: In 2021, a nationwide online survey was conducted with a representative sample of Australians (N = 5100) recruited via market research panels. The results were compared to previous nationwide Australian survey samples from 2018 (N = 1024), 2012 (N = 1200), 2010 (N = 1201) and 2008 (N = 1146). The survey included questions consistent with previous polls regarding self-reported health status and overall opinions of, and confidence in, the Australian health system. RESULTS: There was an increase in the proportion of respondents reporting positive perceptions at each survey between 2008 and 2021, with a significantly higher proportion of respondents expressing a more positive view of the Australian healthcare system in 2021 compared to previous years (χ2(8, N = 9645) = 487.63, p < 0.001). In 2021, over two-thirds of respondents (n = 3949/5100, 77.4%) reported that following the COVID-19 pandemic, their confidence in the Australian healthcare system had either remained the same (n = 2433/5100, 47.7%) or increased (n = 1516/5100, 29.7%). Overall, respondents living in regional or remote regions, younger Australians (< 45 years) and women held less positive views in relation to the system. In 2021, the most frequently identified area for urgent improvement was the need for more healthcare workers (n = 1350/3576, 37.8%), an area of concern particularly for Australians residing in regional or remote areas (n = 590/1385, 42.6%). CONCLUSIONS: Irrespective of disruptions to the Australian healthcare system caused by the COVID-19 pandemic, Australians' perceptions of their healthcare system were positive in 2021. However, concerns were raised about inadequate workforce capacity and the cost of healthcare, with differences identified by age groups and geographical location. PATIENT OR PUBLIC CONTRIBUTION: Health consumer representatives from the Consumers Health Forum of Australia contributed to the co-design, deployment, analysis and interpretation of the results of this survey. J.A. and L.W. from the Consumers Health Forum of Australia contributed to the development of the paper.

Artificial Intelligence-Based Co-Facilitator (AICF) for Detecting and Monitoring Group Cohesion Outcomes in Web-Based Cancer Support Groups: Single-Arm Trial Study.

BACKGROUND: Commonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of nonverbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence-based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics. OBJECTIVE: The aim of this study was to develop a method to train and evaluate AICF's capacity to monitor group cohesion. METHODS: AICF used a text classification approach to extract the mentions of group cohesion within conversations. A sample of data was annotated by human scorers, which was used as the training data to build the classification model. The annotations were further supported by finding contextually similar group cohesion expressions using word embedding models as well. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC). RESULTS: AICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine learning algorithms combined with human input could detect group cohesion, a clinically meaningful indicator of effective OSGs. After retraining with human input, AICF reached an F1-score of 0.82. AICF performed slightly better at identifying group cohesion compared to LIWC. CONCLUSIONS: AICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in web-based settings by attending to individual needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

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.

M-Learning in education during COVID-19: A systematic review of sentiment, challenges, and opportunities.

The flexibility and relatively low cost of mobile devices make educational systems more accessible for learners and educators worldwide. When incorporated with the internet, it creates a better learning environment than the conventional classroom lecture. Many studies have been done to shed insight into the existing state of mobile learning (M-learning) studies. However, further research is needed into this topic at a specific time, i.e., during the COVID-19 pandemic. This study aims to retrieve, review, investigate, and critically assess the existing literature on M-learning that was conducted during the COVID-19 concerning our research theme. This study considered publications from four databases, narrowed our initial search results of 4056 articles down to 83 that are relevant to our research questions, and did an in-depth analysis based on the systematic review protocol. The findings explored the major focusing areas of M-learning applications, the regional sentiment of M-learning users, the determinants and perceptions of M-learning, as well as the benefits, challenges, and opportunities associated with M-learning. This systematic literature review (SLR) was performed to apportion a contribution toward an improved understanding of the basic principles that underpin the rethinking of M-learning applications for policymakers, online course designers, and blended learning facilitators.

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.

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network.

This study explores the use of artificial intelligence (AI) to analyze information from X (previously Twitter) feeds related to COVID-19, specifically focusing on the time following the World Health Organization's (WHO) vaccination announcement. This aspect of the pandemic has not been studied by other researchers focusing on vaccination news. By utilizing advanced AI algorithms, the research aims to examine a wealth of data, sentiments, and trends to enhance crisis management strategies effectively. Our methods involved collecting a dataset of tweets from December 2020 to July 2021. By using specific keywords strategically, we gathered a substantial 15.5 million tweets, focusing on important hashtags like #vaccine and #coronavirus while filtering out irrelevant replies and retweets. The assessment of three different machine learning models-BiLSTM, FFNN, and CNN - highlights the exceptional performance of BiLSTM, achieving an impressive F1-score of 0.84 on the test set, with Precision and Recall metrics at 0.85 and 0.83, respectively. The study provides a detailed visualization of global sentiments on COVID-19 topics, with a main goal of extracting insights to manage public health crises effectively. Sentiment labels were predicted using various classification models and categorized as positive, negative, and neutral for each country after adjusting for population differences. An important finding from the analysis is the variation in sentiments across regions, for instance, with Eastern European countries showing positive views on post-vaccination economic recovery, while China and the United States express negative opinions on the same topic.

Annotated dataset for sentiment analysis and sarcasm detection: Bilingual code-mixed English-Malay social media data in the public security domain.

Sentiment analysis in the public security domain involves analysing public sentiment, emotions, opinions, and attitudes toward events, phenomena, and crises. However, the complexity of sarcasm, which tends to alter the intended meaning, combined with the use of bilingual code-mixed content, hampers sentiment analysis systems. Currently, limited datasets are available that focus on these issues. This paper introduces a comprehensive dataset constructed through a systematic data acquisition and annotation process. The acquisition process includes collecting data from social media platforms, starting with keyword searching, querying, and scraping, resulting in an acquired dataset. The subsequent annotation process involves refining and labelling, starting with data merging, selection, and annotation, ending in an annotated dataset. Three expert annotators from different fields were appointed for the labelling tasks, which produced determinations of sentiment and sarcasm in the content. Additionally, an annotator specialized in literature was appointed for language identification of each content. This dataset represents a valuable contribution to the field of natural language processing and machine learning, especially within the public security domain and for multilingual countries in Southeast Asia.

Analysis of Autistic Adolescents' Essays Using Computer Techniques.

PURPOSE: Challenges associated with narrative discourse remain consistently observable across the entire spectrum of autism. We analyzed written narratives by autistic and non-autistic adolescents and aimed to investigate narrative writing using quantitative computational methods. METHODS: We employed Natural Language Processing techniques to compare 333 essays from students in the final eighth grade of primary school: 195 written by autistic and 138 by non-autistic participants. RESULTS: Autistic students used words with a positive emotional polarity statistically less frequently (p < .001), and their stories were less abstract (p < .001) than those written by peers from the non-autistic group. However, autistic adolescents wrote more complex stories in terms of readability than participants from the non-autistic group (p < .001). The writing competencies assessed by teachers did not differ significantly between the two groups. CONCLUSION: Findings suggest that written narratives by autistic individuals may exhibit characteristics similar to those detected by computational methods in spoken narratives. Collecting data from national exams and its potential usefulness in distinguishing autistic individuals could pave the way for future large-scale and cost-effective epidemiological studies on autism.

The impact of ESG performance on corporate sustainable growth from the perspective of carbon sentiment.

With the increasing importance of environmental and economic sustainability concerns, the concept of Environmental, Social, and Governance (ESG) has gained significant attention. In the era of digitalization, a research approach called carbon sentiment analysis has emerged as an innovative method. This study aims to explore the connections between carbon sentiment, ESG, and corporate sustainable growth within the context of the green economy. By using Ordinary Least Squares (OLS) regression analysis and establishing a panel data model of ESG performance and sustainable growth for Chinese listed companies, a notable positive correlation between the two variables was observed. Endogeneity was addressed using the two-stage instrumental variable method (2SLS) and the dynamic panel Generalized Method of Moments (GMM) model, with the results remaining robust both before and after the COVID-19 pandemic. Carbon-related news and textual information were collected and analyzed using advanced deep learning methods in Natural Language Processing (NLP), specifically Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) models. This analysis enabled sentiment analysis and identification of the sentiment orientation of carbon news. The obtained sentiment scores were then integrated with company data to establish a moderation effect model. The findings of the study reveal that carbon sentiment significantly and positively moderates ESG performance in relation to corporate sustainable growth. Furthermore, the construction of a mediation effect model showed that carbon sentiment can moderate ESG performance by reducing environmental uncertainty, enhancing social trust, and alleviating financing constraints, thereby influencing corporate sustainable growth. The results of the heterogeneity group regression analysis demonstrate that the impact of ESG performance driven by carbon sentiment on sustainable growth is more pronounced in carbon market pilot regions, non-heavily polluting industries, and labor-intensive industries. This research provides a fresh perspective for understanding the dynamics of ESG, online carbon sentiment, and their implications for corporate sustainable growth. Additionally, it contributes to the development of the green economy and the formulation of environmental management policies.

Deep learning in finance assessing twitter sentiment impact and prediction on stocks.

The widespread adoption of social media platforms has led to an influx of data that reflects public sentiment, presenting a novel opportunity for market analysis. This research aims to quantify the correlation between the fleeting sentiments expressed on social media and the measurable fluctuations in the stock market. By adapting a pre-existing sentiment analysis algorithm, we refined a model specifically for evaluating the sentiment of tweets associated with financial markets. The model was trained and validated against a comprehensive dataset of stock-related discussions on Twitter, allowing for the identification of subtle emotional cues that may predict changes in stock prices. Our quantitative approach and methodical testing have revealed a statistically significant relationship between sentiment expressed on Twitter and subsequent stock market activity. These findings suggest that machine learning algorithms can be instrumental in enhancing the analytical capabilities of financial experts. This article details the technical methodologies used, the obstacles overcome, and the potential benefits of integrating machine learning-based sentiment analysis into the realm of economic forecasting.

An Al-BERT-Bi-GRU-LDA algorithm for negative sentiment analysis on Bilibili comments.

The number of online self-learning users has been increasing due to the promotion of various lifelong learning programs. Unstructured commentary text related to their real learning experience regarding the learning process is generally published by users to show their opinions and complaints. The article aims to utilize the dataset of real text comments of 10 high school mathematics courses participated by high school students in the Bilibili platform and construct a hybrid algorithm called the Artificial Intelligence-Bidirectional Encoder Representations from Transformers (BERT) + Bidirectional Gated Recurrent Unit (BiGRU) and linear discriminant analysis (LDA) to crunch data and extract their sentiments. A series of tests regarding algorithm comparison were conducted on the educational review datasets. Comparative analysis found that the proposed algorithm achieves higher precision and lower loss rates than other alternative algorithms. Meanwhile, the loss ratio of the proposed algorithm was kept at a low level. At the topic mining level, the topic clustering of negative comments found that the barrage content was very messy and the complexity of the course content was generally reported by the students. Some problems related to videos were also mentioned. The outcomes are promising that the fundamental issues underlined by the students can be effectively resolved to improve curriculum and teaching quality.

A Comparative Sentiment Analysis of Greek Clinical Conversations Using BERT, RoBERTa, GPT-2, and XLNet.

In addressing the critical role of emotional context in patient-clinician conversations, this study conducted a comprehensive sentiment analysis using BERT, RoBERTa, GPT-2, and XLNet. Our dataset includes 185 h of Greek conversations focused on hematologic malignancies. The methodology involved data collection, data annotation, model training, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and specificity. BERT outperformed the other methods across all sentiment categories, demonstrating its effectiveness in capturing the emotional context in clinical interactions. RoBERTa showed a strong performance, particularly in identifying neutral sentiments. GPT-2 showed promising results in neutral sentiments but exhibited a lower precision and recall for negatives. XLNet showed a moderate performance, with variations across categories. Overall, our findings highlight the complexities of sentiment analysis in clinical contexts, especially in underrepresented languages like Greek. These insights highlight the potential of advanced deep-learning models in enhancing communication and patient care in healthcare settings. The integration of sentiment analysis in healthcare could provide insights into the emotional states of patients, resulting in more effective and empathetic patient support. Our study aims to address the gap and limitations of sentiment analysis in a Greek clinical context, an area where resources are scarce and its application remains underexplored.

Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning.

Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public's opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications.