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Multimodal sentiment analysis (MSA) seeks to understand human affection by leveraging signals from multiple modalities. A core challenge in MSA is the effective extraction of sentimental relations between these signals, as this can enhance a model's consistency and accuracy. Existing studies typically use multimodal matching tasks to learn all semantic relations between modalities and then use downstream task to obtain the specific semantics from the multimodal representation. However, there are multiple semantics between modalities, such as action semantics, scene semantics and sentiment semantics. Relying solely on specific tasks to filter these semantics often results in a surplus of redundant information in the multimodal representation, potentially degrading MSA accuracy. In addition, the unimodal semantic expression is also important. In this paper, we propose a semantic-specific multimodal relation learning method to correlate modalities with specific semantics. Specifically, with smaller computational resources, we enhance unimodal sentimental semantic expression while diminishing non-sentimental semantic information in the multimodal representation. We conducted experiments on multimodal sentiment analysis datasets, CMU-MOSI, CMU-MOSEI and CH-SIMS. The results show that our method outperforms the current state-of-the-art. Notably, on the Acc2 evaluation metric, our approach exhibits an average accuracy improvement of 0.75 compared to the best baseline. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is expected to transform many facets of our existence with the gradual penetration of the semantic web. One such AI-based technology is a chatbot that considerably alters numerous business verticals to enhance users and businesses. Even though companies are extensively contemplating using chatbots to supplement or even replace humans, users have exhibited anger, confusion, and customer dissatisfaction with chatbots. The current study aims to find the reasons for user frustration in the case of conversational chatbots and adopts exploratory research using a critical incident technique (CIT) survey. Results are analysed using sentiment analysis and topic modelling with Python. Results of the study indicate four significant causes of user frustration, i.e. poor semantic understanding, lack of customisation, lack of Humanisation of the bot, and poor competency. These causes have further been examined across various socio-demographic variables and industry contexts. This work would theoretically advance the bot literature and fulfil the gap by understanding the reasons for frustration. This would further help bot developers and marketers in designing and training chatbots that lead to better engagement and enhanced customer experience. [ABSTRACT FROM AUTHOR]

With the consumption pattern of online shopping becoming increasingly common, the way for consumers to obtain product evaluation information has changed from word of mouth in the past to online reviews. More than 70% of consumers refer to e-commerce platform product evaluation information when shopping online, and more than 90% of enterprises believe that reviews will play a decisive role in future consumer behavior. Unlike subjective survey data, product online reviews are not subject to the subjective judgment of researchers during the survey process, and can reflect the real user experience and emotional tendency. Therefore, it is important to study the ways in which user evaluation big data drive product design research and development, such as accelerating the shift in product design, promoting marketing, and improving user satisfaction. Current research on running shoes mainly focuses on product function development, shoe last redesign and market demand classification. At present, no scholars have explored the factors that consumers pay attention to when buying and using running shoes from the perspective of e-commerce big data. Understanding the consumption trend and consumer preference of running shoes is of great significance for industrial development and marketing strategy formulation. To mine consumers’ attention information in their buying running shoes online, firstly, Requests library and Pymysql library in python3.11 were used to collect the sales feature data of top 600 running shoes sold on Jingdong Mall and 100,000 user comments data. Secondly, text preprocessing of online review text was performed by using the precise mode in the Chinese word segmentation system of Jieba Database. Thirdly, Origin 2021 was used to analyze the basic information of the sales characteristics of running shoes. Fourthly, LDA model and Gibbs sampling were used to cluster review texts to explore the distribution of product feature words under different themes. Finally, SnowNLP was used to score the text for emotion, so as to obtain positive and negative labels, and topic analysis was performed based on emotion labels to compare the difference in topic distribution of positive and negative comments. From the perspective of big data analysis, this paper used LDA model to conduct text mining on 100,000 online reviews of running shoes, conduct word frequency co-occurrence analysis, topic clustering and sentiment analysis on product review data, analyze the causes of problems from the dimensions of brand, technology and after-sales service and put forward relevant suggestions. Domestic running shoes have a complete product layout from the entry market to the high-end market, but due to the world brand effect, technology accumulation and user reputation, there is still a long way to go compared with the top brands in sales and focusing on the mid-to-high-end market. Most of the running shoes with top sales rank participate in full reduction and coupon activities, and the proportion of self-operated, store sales and quality certification labels is higher than the overall level, while self-operated and coupon labels have a significant role in promoting the purchase of running shoes. When consumers buy running shoes online, they mainly pay attention to the appearance details, functional attributes, cost performance, wearing feelings, service concessions and so on. A small number of consumers have a poor attitude towards the wearing experience of running shoes, product quality and service promotion. In the future, in-depth research can be carried out according to various characteristics of consumers. On the basis of collecting big data of user comments, it is necessary to further mine user information such as age, region and occupation, so that the comment topic can be mapped to specific user groups, which is helpful to meet the targeted research and development of specific consumer groups and the implementation of precision marketing. In addition, to make the results more universally valuable, it is necessary to continue to increase the amount of data in the future to make the model better understand the various topics, domains and contexts, so as to improve the reliability and validity of the results. [ABSTRACT FROM AUTHOR]

In this article we address the problem of sentimentality in teaching and learning difficult histories in higher education. While sentimentality has been widely theorised, empirical research is scarce. Here we contribute an empirical investigation describing how university students in an Argentinian setting engaged with histories of loss, death, and suffering through specific pedagogical activities in ways that minimised the risks of 'empty sentimentality'. These tasks fostered students' use of linguistic and non-linguistic (artistic, semiotic) means of meaning-making in connection with difficult histories and enabled deeper emotional engagement. The study shows that the creation of critical affective spaces in the classroom cultivated students' responsibility for engaging in transformative action. We suggest that educators need to explicitly challenge the often moralising and self-indulgent character of teaching and learning difficult histories, as this character is constitutive of the very sentimentality with which pedagogical practice is so frequently charged. [ABSTRACT FROM AUTHOR]

Drawing on Chantel Mouffe, Mikhail Bakhtin, Judith Butler and others, we examined how English-speaking YouTube users discussed the Wuhan lockdown from late January to June 2020. We argue that news-prompted public spheres are affective, contextualised and short-lived and our findings suggest that the severity of the COVID-19 pandemic outside of China is closely related to the worsening sentiments of English-speaking publics on YouTube. Comments are also significantly associated with the changing severity of the pandemic, reflecting the changing concerns of publics regardless of the central theme of news stories. The divergence of the sentiment scores of user comments is also salient among different media spaces. These findings suggest that a de-localised, universal global public sphere is misleading and more nuanced and contextualised studies are warranted. [ABSTRACT FROM AUTHOR]

This study explores the effect of unprecedented mass isolation during COVID-19 lockdowns through the lens of self-disclosure of loneliness on Twitter. Using a dataset of 30 million public tweets, we use machine learning to identify tweets that contain self-disclosure of loneliness. We find that thousands more people turned to Twitter to express their loneliness during the lockdowns; however, this effect normalized within a month, demonstrating the "ordinization" effect on a collective level. Furthermore, lockdown brought a marked shift in the weekly timings of posting and a change in the accompanying emotions, which were more positive and other-focused. Finally, based on a qualitative analysis, we propose an updated typology of loneliness that captures the possibilities offered by the affordances of social media. Our findings illustrate the profound effect lockdowns had on the societal psychological state and emphasize the importance of mental health resources during extreme and isolating events. [ABSTRACT FROM AUTHOR]

Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture‐based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions. [ABSTRACT FROM AUTHOR]

Amid a heightened focus on sustainable consumption, restaurants are increasingly adopting green practices. Yet, understanding determinants of satisfaction in green restaurants remains unexplored. Analyzing 85,337 TripAdvisor reviews from US Green Restaurant Association certified restaurants, and leveraging BERTopic and aspect-based sentiment analysis, our study reveals previously unidentified subtopics, like "pet-friendly," and indicates that after food, value, and service, green attributes significantly affect satisfaction, surpassing atmosphere. Additionally, we studied ramifications of not mentioning aspects in reviews, showing an insignificant difference in satisfaction between reviews without green attributes and those with neutral sentiment scores, highlighting the importance of promoting and delivering green initiatives. [ABSTRACT FROM AUTHOR]

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as "tasty", and the influence of neutral words like "ok" on judgment variation. The study's methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation. [ABSTRACT FROM AUTHOR]

Agent-based models of opinion formation are becoming increasingly complex, because of their size and of the embedding of several individual psychological traits of the agents, aimed at realistically capturing the multifaceted aspects of social interaction. Therefore, the characterisation of the model properties mostly relies on simulation-based numerical approaches: more techniques are needed to analyse, contrast, and compare the properties of different models. We propose a novel graphical technique, which relies on the Agreement Plot to visualise the evolution of opinion distributions over time, that allows us to unveil behavioural patterns and capabilities of agent-based opinion formation models. Our proposed approach can be used to characterise the relation between global properties of the model evolution and the model features (initial opinion distributions, agent parameters, underlying digraphs), and is here showcased through its application to both seminal and recently proposed opinion formation models. [ABSTRACT FROM AUTHOR]

Citation illustrates the link between citing and cited documents. Different aspects of achievements like the journal's impact factor, author's ranking, and peers' judgment are analyzed using citations. However, citations are given the same weight for determining these important metrics. However academics contend that not all citations can ever have equal weight. Predominantly, such rankings are based on quantitative measures and the qualitative aspect is completely ignored. For a fair evaluation, qualitative evaluation of citations is needed in addition to quantitative ones. Many existing works that use qualitative evaluation consider binary class and categorize citations as important or unimportant. This study considers multi-class tasks for citation sentiments on imbalanced data and presents a novel framework for sentiment analysis in in-text citations of research articles. In the proposed technique, features are retrieved using a convolutional neural network (CNN), and classification is performed using a voting classifier that combines Logistic Regression (LR) and Stochastic Gradient Descent (SGD). The class imbalance problem is handled by the synthetic minority oversampling technique (SMOTE). Extensive experiments are performed in comparison with the proposed approach using SMOTE-generated data and machine learning models by term frequency (TF), and term frequency-inverse document frequency (TF-IDF) to evaluate the efficacy of the proposed approach for citation analysis. It is found that the proposed voting classifier using CNN features achieves an accuracy, precision, recall, and F1 score of 0.99 for all. This work not only advances the field of sentiment analysis in academic citations but also underscores the importance of incorporating qualitative aspects in evaluating the impact and sentiments conveyed through citations. [ABSTRACT FROM AUTHOR]

Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this opinion is useful because it can provide insight into developers' feedback about various tools and topics. General-purpose sentiment analysis tools do not work well in the software domain because most of these tools are trained on movies and review datasets. Therefore, efforts are underway to develop domain-specific sentiment analysis tools for the Software Engineering (SE) domain. However, existing domain-specific tools for SE struggle to compute negative and neutral sentiments and can not be used on all SE datasets. This work uses a hybrid technique based on deep learning and a fine-tuned BERT model, i.e., Bert-Base, Bert-Large, Bert-LSTM, Bert-GRU, and Bert-CNN presented that is adapted as a domain-specific sentiment analysis tool for Community Question Answering datasets (named as Fuzzy Ensemble). Five different variants of fine-tuned BERT on the SE dataset are developed, and an ensemble of these fine-tuned models is taken using fuzzy logic. The trained model is evaluated on four publicly available benchmark datasets, i.e., Stack Overflow, JavaLib, Jira, and Code Review, using various evaluation metrics. The fuzzy Ensemble model is also compared with the state-of-the-art sentiment analysis tools for the software engineering domain, i.e., SentiStrength-SE, Senti4SD, SentiCR, and Generative Pre-Training Transformer (GPT). GPT mode is fine-tuned by the authors for domain-specific sentiment analysis. The Fuzzy Ensemble model covers the limitation of existing tools and improve accuracy to predict neutral sentiments even on diverse dataset. The fuzzy Ensemble model performs superior to state-of-the-art tools by achieving a maximum F1-score of 0.883. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people’s opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data’s are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated. [ABSTRACT FROM AUTHOR]

In the last few years, various topics, including sports, have seen social media platforms emerge as significant sources of information and viewpoints. Football fans use social media to express their opinions and sentiments about their favourite teams and players. Analysing these opinions can provide valuable information on the satisfaction of football fans with their teams. In this article, we present Soutcom, a scalable real‐time system that estimates the satisfaction of football fans with their teams. Our approach leverages the power of social media platforms to gather real‐time opinions and emotions of football fans and applies state‐of‐the‐art machine learning‐based sentiment analysis techniques to accurately predict the sentiment of Arabic posts. Soutcom is designed as a cloud‐based scalable system integrated with the X (formerly known as Twitter) API and a football data service to retrieve live posts and match data. The Arabic posts are analysed using our proposed bidirectional LSTM (biLSTM) model, which we trained on a custom dataset specifically tailored for the sports domain. Our evaluation shows that the proposed model outperforms other machine learning models such as Random Forest, XGBoost and Convolutional Neural Networks (CNNs) in terms of accuracy and F1‐score with values of 0.83 and 0.82, respectively. Furthermore, we analyse the inference time of our proposed model and suggest that there is a trade‐off between performance and efficiency when selecting a model for sentiment analysis on Arabic posts. [ABSTRACT FROM AUTHOR]

Background: The aim of the How Farm Vets Cope project was to co-design, with farm veterinary surgeons, a set of web-based resources to help them and others deal with the different situations that they can face. As part of the wider project, participants were recruited for one-to-one semi-structured phone interviews. These interviews focused on elements of job satisfaction and how the participants coped during periods of poor mental wellbeing or with setbacks and failure. Methods: Transcripts of these interviews were analysed using both quantitative methods of sentiment analysis and text mining, including term frequency/inverse document frequency and rapid automated keyword extraction, and qualitative content analysis. The twin aims of the analysis were identifying the important themes discussed by the participants and comparing the results of the two methods to see what differences, if any, arose. Results: Analysis using the afinn and nrc sentiment lexicons identified emotional themes of anticipation and trust. Rapid automated keyword extraction highlighted issues around age of vets and support, whilst using term frequency/inverse document frequency allowed for individual themes, such as religion, not present across all responses, to be identified. Content analysis supported these findings, pinpointing examples of trust around relationships with farmers and more experienced vets, along with some examples of the difference good support networks can make, particularly to younger vets. Findings: This work has confirmed previous results in identifying the themes of trust, communication and support to be integral to the experience of practicing farm veterinary surgeons. Younger or less experienced vets recognised themselves as benefiting from further support and signposting, leading to a discussion around the preparation of veterinary students for entry into a farm animal vet practice. The two different approaches taken showed very good agreement in their results. The quantitative approaches can be scaled to allow a larger number of interviews to be utilised in studies whilst still allowing the important qualitative results to be identified [ABSTRACT FROM AUTHOR]

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers' method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions. [ABSTRACT FROM AUTHOR]

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO's efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field. [ABSTRACT FROM AUTHOR]

Background: Medical Waste (MW), conceptualized as waste generated in the diagnosis, treatment, or immunization of human beings or animals, posing massive threat to public health. Environment-friendly public attitudes promotes the shaping of pro-environmental behavior. However, the public attitudes of MW and the potential determinants remained scarce. The present study aims to reveal globally public attitudes towards MW and captured the determinants. Methods: We integrated the crawler technology with sentiment analysis to captured the public attitudes toward MW across 141 specific countries from 3,789,764 related tweets. Multiple cross-national databases were integrated to assess characteristics including risk, resistance, environment, and development. The spatial regression model was taken to counterbalence the potential statistical bias. Results: Overall, the global public attitudes towards MW were positive, and varied significantly across countries. Resilience (β = 0.78, SD = 0.14, P < 0.01) and development (β = 1.66, SD = 0.13, P < 0.01) posed positive influence on public attitudes towards MW, meanwhile, risk (β = -0.1, SD = 0.12, P > 0.05) and environment (β = 0.09, SD = 0.09, P > 0.05) were irrelated to the shaping of positive MW public attitudes. Several positive moderating influences was also captured. Additionally, the cross-national disparities of the determiants were also captured, more specific, public attitudes towards MW in extremely poor areas were more likely to be negatively affected by risks, resilience and development. Conclusions: This study focused mainly on the public attitudes as well as captured the potential determinants. Public attitudes towards MW were generally positive, but there were large cross-national disparities. Stakeholders would need to designate targeted strategies to enhance public satisfaction with MW management. [ABSTRACT FROM AUTHOR]

The problem of stock price prediction has been a hot research issue. Stock price is influenced by various factors at the same time, and market sentiment is one of the most critical factors. Financial texts such as news and investor comments reflect investor sentiment in the stock market and influence market movements. Previous research models have struggled to accurately mine multiple sources of market sentiment information originating from the Internet and traditional sentiment analysis models are challenging to quantify and combine indicator data from market data and multi-source sentiment data. Therefore, we propose a BERT-LLA stock price prediction model incorporating multi-source market sentiment and technical analysis. In the sentiment analysis module, we propose a semantic similarity and sector heat-based model to screen for related sectors and use fine-tuned BERT models to calculate the text sentiment index, transforming the text data into sentiment index time series data. In the technical indicator calculation module, technical indicator time series are calculated using market data. Finally, in the prediction module, we combine the sentiment index time series and technical indicator time series and employ a two-layer LSTM network prediction model with an integrated attention mechanism to predict stock close price. Our experiment results show that the BERT-LLA model can accurately capture market sentiment and has a strong practicality and forecasting ability in analyzing market sentiment and stock price prediction. [ABSTRACT FROM AUTHOR]

The design of 3D scenes is of great significance, and one of the crucial areas is interior scene design. This study not only pertains to the living environment of individuals but also has applications in the design and development of virtual environments. Previous work on indoor scenes has focused on understanding and editing existing indoor scenes, such as scene reconstruction, segmentation tasks, texture, object localization, and rendering. In this study, we propose a novel task in the realm of indoor scene comprehension, amalgamating interior design principles with professional evaluation criteria: 3D indoor scene design assessment. Furthermore, we propose an approach using a transformer encoder–decoder architecture and a dual-graph convolutional network. Our approach facilitates users in posing text-based inquiries; accepts input in two modalities, point cloud representations of indoor scenes and textual queries; and ultimately generates a probability distribution indicating positive, neutral, and negative assessments of interior design. The proposed method uses separately pre-trained modules, including a 3D visual question-answering module and a dual-graph convolutional network for identifying emotional tendencies of text. [ABSTRACT FROM AUTHOR]

ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT's ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system. [ABSTRACT FROM AUTHOR]

The electricity market in Spain holds significant importance in the nation's economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country's ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI's ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) constitutes a significant field within natural language processing (NLP). This study proposes a multi-strategy text data augmentation methodology to overcome challenges such as limited dataset sizes and the absence of comprehensive, high-quality corpora in aspect-level sentiment classification (ASC). Specifically, it expands the SemEval 2014 Restaurant and Laptop training datasets from 3017 to 4646 instances and from 1864 to 3693 instances, respectively. The methodology encompasses both word-level and sentence-level augmentations. Evaluations using advanced deep learning techniques, including Att-LSTM, SVM, CNN, and Bi-LSTM, were conducted on the enhanced SemEval 2014 Task 4 restaurant dataset and laptops dataset. The test dataset sizes are 920 and 668, respectively. The Att-LSTM, demonstrating superior performance, recorded F1 score improvements of 5.0% and 4.4% on the restaurant and laptop datasets, respectively, following the application of the multi-strategy augmentation methodology compared to others. This approach significantly enlarges the dataset and improves performance in ASC tasks. [ABSTRACT FROM AUTHOR]

Background: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. Methods: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. Results: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. Conclusions: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks. [ABSTRACT FROM AUTHOR]

The challenges faced by ethnic migrants in urban areas highlight the importance of gaining a comprehensive understanding of their perception. This research investigates the adaptive capacities of ethnic migrants, extending the analysis beyond the first generation to explain how these communities navigate challenges in the post-COVID era. The study examines the dynamics of ethnic migration, exploring intercultural interactions and the evolving experiences of successive migrant generations using sentiment analysis techniques. The study analyzes textual data from in-depth semi-structured interviews to understand their experiences in city and sentiments about the host community. Sentiment analysis, often known as opinion mining, is used in this research to determine the emotional undertone of the third and fourth-generation Chinese and Anglo-Indian residents living in Kolkata, India. The study contributes to evidence-based strategies by identifying positive and negative sentiments associated with ethnic migrants. Results show that the frequency and polarity of the words used during the interviews significantly explain their association with the host community and their sense of belongingness. The findings of this research are significant in indicating the vitality of these communities in the city. [ABSTRACT FROM AUTHOR]

An analysis of expert perspectives on groundwater governance arrangements in South Africa is presented, particularly those arrangements that are pertinent to the complex and socially and ecologically significant implications of exploiting unconventional oil and gas (UOG). The paper presents a detailed assessment of literature on groundwater governance research, the findings of which are applied as a framework for a series of expert interviews, comprising hydrogeologists, lawyers, engineers, and governance specialists. This methodological approach was adopted as a means to enable an analysis of opinions on the current situation of groundwater governance in South Africa and how fit-for-purpose this is for managing the exploitation of UOG. The analysis was also informed by observation of participants at several relevant decision-making and stakeholder events. Whilst the findings indicated a generally positive evaluation of the initial steps taken to assess UOG impacts and engage relevant communities, recurrent criticisms also are featured across the interviews. Key implications arising from the research include: (1) the need for continued stakeholder engagement, and government follow-through on the outcomes of these processes, (2) the necessity for detailed groundwater-specific regulations to be drafted at the earliest opportunity, to ensure that the energy policy vacuum does not have a negative knock-on effect for effective groundwater management, and (3) the prevalence of significant governance gaps, particularly regarding regulatory and institutional capacity, and the need for continued development of a functional network of institutions to effectively manage UOG exploitation alongside groundwater resources. [ABSTRACT FROM AUTHOR]

In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards. [ABSTRACT FROM AUTHOR]

China has actively internationalized its doctoral education by funding students to study abroad via the China Scholarship Council. Nevertheless, little is known about the scholarship recipients' perceptions of their study sojourn, especially those on short-term exchange programs. Based on 100 visiting doctoral students' study reports, the study employed a sentiment analysis and was guided by the ecological system theory to untangle how this cohort affectively evaluated academic and non-academic dimensions of the study sojourn as a multi-layered ecological system. Via a computer-based SKEP analysis, the study contributes new objective insights regarding the perceived nature of an overseas sojourn, which is predominantly positive. In particular, it reveals positive perceptions regarding the funder and institutional prestige, indicating that the recipients' increasing national pride may be harnessed as a soft pulling force for nation-building. This paper ends by offering implications that may potentially benefit doctoral education in China and other countries. [ABSTRACT FROM AUTHOR]

Artificial intelligence methods based on deep learning (DL) have recently made significant progress in many different areas including free text classification and sentiment analysis. We believe that corporate governance is one of these areas, where DL can generate very valuable and differential knowledge, for example, by analyzing the biographies of independent directors, which allows for qualitative modeling of their profile in an automatic way. For this technology to be accepted it is important to be able to explain how it generates its results. In this work we have developed a six-dimensional labeled dataset of independent director biographies, implemented three recurrent DL models based on LSTM and transformers along with four ensembles, one of which is an innovative proposal based on a multi-layer perceptron (MLP), trained them using Spanish language and economics and finance terminology and performed a comprehensive test study that demonstrates the accuracy of the results. We have also performed a complete study of explainability using the SHAP methodology by comparatively analyzing the developed models. We have achieved a mean error (MAE) of 8% in the modeling of the open text biographies, which has allowed us to perform a case study of time analysis that has detected significant variations in the composition of the Standard Expertise Profile (SEP) of the boards of directors, related to the crisis of the period 2008–2013. This work shows that DL technology can be accurately applied to free text analysis in the finance and economic domain, by automatically analyzing large volumes of data to generate knowledge that would have been unattainable by other means. [ABSTRACT FROM AUTHOR]

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains. [ABSTRACT FROM AUTHOR]

ChatGPT, an AI chatbot developed by OpenAI, was released in November 2022, sparking a significant surge in global awareness and utilization of generative AI across various domains. Although recent studies have acknowledged the significance of ChatGPT in the education sector, they have yet to focus on exploring faculty attitudes toward ChatGPT. We gathered a comprehensive corpus of tweets containing "#ChatGPT" and "#highered" between November 30th, 2022, and April 30th, 2023. We analyzed data by triangulating VADER, NRC lexicon, and ground coding. Findings suggest that 40% of the expressed sentiments were positive, 51% were neutral, and 9% were negative. The study also revealed the diverse range of emotions held by higher education faculty regarding ChatGPT, with trust and joy being the most prevalent positive sentiments and fear and anger being the most prevalent negative sentiments. This study shed light on faculty members' perceptions of ChatGPT, contributing to a better understanding of the impact, emotions, and incorporation of ChatGPT in the higher education sector. [ABSTRACT FROM AUTHOR]

In recent years, an exponential increase in the usage of social network services has been observed. These community services are typically used through different applications including personal computers, multiple applications of modern smartphones and wearable technologies. Proper identification and separation of different languages text, topic-based classification of text and classification of active users based on their published comments and posts are major challenges. In this research, our primary focus is to deal with English text collected through different IoT applications to analyse posts/comments to categorise people's opinion in politics. We have developed an IoT framework model for collecting data from social media especially Facebook, preprocessed and clean data to be used for analysis, and separation of data based on different languages. Sentiment analysis techniques are used to detect polarisation of the individual user. The proposed system clustered IoT individuals based on their comments and posts and successfully detected political polarisation. The proposed approach obtained encouraging results with a precision of 66.7%, a recall of 71.4%, and an F-measure of 69.0% in the case of annotated data of 50 users and a precision of 75.0%, a recall of 87.1%, and F-measure of 80.6% in the case of annotated data of 100 users. [ABSTRACT FROM AUTHOR]