Neutral Sentiment Research Articles

Analisis Sentimen Masyarakat Berdasarkan Komentar Kerja Sama Tiktok Shop dan Tokopedia di Instagram Menggunakan Metode Naïve Bayes Classifier

The reopening of the Tiktok Shop and collaboration with Tokopedia has caught the public's attention. That's proven by a post on Instagram about their collaboration that received various responses. This research aims to conduct sentiment analysis to ascertain whether the public approves of the two's partnership. The Naïve Bayes Classifier method was used to analyze 641 comment data from the period 11 December 2023 to 11 February 2024. The results show the composition of positive, negative, neutral sentiment and unclassified data, as well as accuracy, precision and recall. Of the 641 data recorded, there were 269 neutral sentiment data, 194 negative sentiment data, 176 positive sentiment data, and 437 unclassified data. The Naïve Bayes Classifier model with oversampling techniques obtained an accuracy of 83%, precision of 81%, and recall of 93%.

Analytical Study for Prjice Prediction of Bitcoin Using Machine Learning and Artificial Intelligence

Abstract: Cryptocurrency transactions rely on encryption algorithms for security and decentralization. However, traditional machine learning algorithms often struggle with the dynamic cryptocurrency market, leading to inaccuracies. To address this, integrating Integrating sentiment analysis of Twitter data alongside Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models enriches the analysis of cryptocurrency market dynamics. Twitter provides dataset from kaggle open source and the sentiment expressed by users on cryptocurrencies, enabling the prediction of tweet sentiment (positive, negative, or neutral) through Natural Language Processing (NLP). This integration enhances understanding of market sentiment's impact on cryptocurrency prices: positive sentiment may drive prices up, negative sentiment may lead to declines, and neutral sentiment may indicate stability. By analyzing sentiment alongside historical trends and emerging patterns, users gain a holistic view of cryptocurrency markets. This approach aids decision-making, improving transaction accuracy and efficiency. Ultimately, the combination of sentiment analysis with BiLSTM and BiGRU models advances the comprehension of cryptocurrency market dynamics, enhancing user insights and facilitating informed decisions in the volatile cryptocurrency ecosystem.

Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia

As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset.

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.

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.

Analysis of green deal communication on twitter: environmental and political perspective

The Green Deal and its implementation are generating discussions across society. Changes brought about by the agreement could impact sustainable development worldwide; therefore, identifying the most common Green Deal-related topics on a global scale can offer insight into the public mood around implementation of the agreement. Social networks provide the opportunity to find such topics because they contain a large amount of data produced by users worldwide: analysis of their content can therefore provide insight into the discourse on the Green Deal and identify the sentiment in discussions around this topic. In this article, we present perceptions of the Green Deal and identify the main Green Deal-related topics based on analysis of communication on the Twitter social network (currently X social network). Using the search terms “green deal,” “greendeal,” and “#greendeal,” 192,567 tweets from 89,328 unique users were captured between 1 January 2019 and 31 March 2023. We identified the 40 most used unique hashtags that people used when communicating about the Green Deal, which included “#EU,” “#eugreendeal,” and “#climatechange,” and the 16 most relevant topics discussed in relation to the Green Deal, which included both European (“European Green Deal”) and North American (“Green New Deal”) perspectives. Each topic was associated with a certain amount of negative, positive, or neutral sentiment: the most positive sentiment was associated with the “Industrial plan” and “Hydrogen” topics, and the most negative sentiment was associated with topics relating to “Joe Biden” and “Alexandria Ocasio-Cortez.” Overall, our analysis of the discourse regarding the Green Deal offers organizations and decision-makers insight into how people perceive different aspects of the Green Deal and related topics. This may be beneficial in tackling disinformation across social networks and increasing public awareness, which could create a society better equipped to face the global concern of climate change.

ChatGPT in Education

AI chatbots, e.g. ChatGPT, are becoming increasingly popular in education as a means to enhance student learning experiences and improve teaching efficiency. This study utilizes NVivo 12 Plus to examine the role of AI chatbots in education, ethical considerations, and sentimental analysis regarding the utilization of ChatGPT in education. ChatGPT has revolutionized education, but their use raises ethical concerns. They can enhance language learning, but may lead to plagiarism and information overload. Students may not develop discrimination skills and may rely on ChatGPT, leading to concerns about academic dishonesty and a failure to develop cognitive and analytical skills. The use of ChatGPT in clinical education also raises accountability and liability concerns regarding the use of patient information for educational purposes. Guidelines should be established to ensure privacy rights are upheld. Finally, the positive sentiment category was populated by predominantly positive sentiments, followed by neutral and negative sentiments. Future research on ChatGPT in education should focus on its application effectiveness in various educational settings and ethical considerations.

Exploring the public's beliefs, emotions and sentiments towards the adoption of the metaverse in education: A qualitative inquiry using big data

AbstractThe metaverse is rapidly reshaping our understanding of education, yet identifying the public's beliefs, emotions and sentiments towards its adoption in education remains largely uncharted empirically. Grounded in the Technology Acceptance Model (TAM) and Digital Diffusion Theory (DOI), this paper aims to fill this gap using a big‐data approach and machine learning to scrape comments made by social media users on recent popular posts or videos related to adopting the metaverse in education from three prominent social media platforms. The cleaning process narrowed down 11,024 comments to 4277, then analysed them using thematic, emotion and sentiment analysis techniques. The thematic analysis revealed that adopting the metaverse in education evokes a complex range of public beliefs: (1) innovative learning methods; (2) accessibility and inclusion; (3) concerns about quality and effectiveness; (4) technological challenges and the digital divide; (5) the future of work and skills; and (6) privacy and security concerns. Integrating these themes with emotion and sentiment analyses reveals a landscape of a significant portion of neutral sentiments that corroborates enthusiasm attenuated by caution. This careful consideration stresses the urgent need for a balanced approach to adopting the metaverse in education to ensure that resulting educational advancements benefit all learners equitably. As one of the first studies to offer a multidimensional view of the public's beliefs about metaverse education using big data, this research not only contributes to TAM and DOI but also provides critical insights that could inform policy, enhance educational practices and guide future scholarship in this emerging field.

A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study.

Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes. This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers. Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets. Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues. This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users.

Abstract PO5-12-07: Analyzing Patient Experiences with Adjuvant Hormone Therapy for Early Breast Cancer Treatment via NLP/ML on Social Media: Symptoms and Their Mitigation

Abstract Understanding the early breast cancer (eBC) patient experience of disease and treatment is critical to addressing unmet patient needs, improving outcomes, and informing the next wave of drug development. Traditional approaches, such as interviews or focus group studies, are resource intensive and have limitations in reflecting the diverse experiences of patients with different disease characteristics, treatments, and sociodemographic backgrounds, and in detecting rare severe events that could potentially lead to therapy discontinuation. To overcome these limitations, we have developed a semi-automated approach utilizing Natural Language Processing (NLP) and Machine Learning (ML) methods in application to public patient forums to extract patient insights. Specifically, we analyzed 500,000 anonymized posts from the BreastCancer.org forum, utilizing Transformer-based methods to identify posts related to eBC patient experiences treated with standard hormonal therapies (HT). First, a novel ML-based model identified posts related to eBC adjuvant HT. For those, patient demographics, symptoms and their severity, and symptom management strategies such as exercise and yoga were subject to further analysis. Out of 362,074 relevant eBC posts, 32 symptoms and 9 impacts were significantly associated with at least one of the six hormonal therapies we considered. Hot flushes (relative risk [RR]: 6.70; 95% CI: 3.36–13.36), arthralgia (RR: 6.67; 95% CI: 3.53–12.59), weight increase (RR: 4.83; 95% CI: 3.20–7.28), mood swings (RR: 7.36; 95% CI: 5.75–9.42), insomnia (RR: 4.76; 95% CI: 3.14–7.22), and depression (RR: 3.05; 95% CI: 1.71–5.44) demonstrated the strongest associations. Severe headache, dizziness, back pain, and muscle spasms showed significant associations with ≥1 HT despite their low overall prevalence in eBC posts. Notably, posts contained a wide range of symptoms severity, including rarely seen symptoms in smaller patient pools, such as severe night hot flashes leading to treatment discontinuation. Symptoms/impacts mentioned in association with exercise and yoga as a way for symptom mitigation included hot flashes, arthralgia, fatigue, anxiety, depression, and pain. Of those posts, 53.3% to 82.5% reported positive or neutral sentiments for exercise, and 22.2% to 100.0% for yoga. For posts specifically mentioning the management of side effects of HT, between 50.0% and 63.5% had a positive sentiment associated with exercise concerning each of the symptoms/impacts with a lower positive impact related to yoga (between 11.1% and 43.0% of posts). Posts discussing pain management and exercise included a higher proportion of negative feedback, versus positive sentiment for depression. Posts related to yoga as an HT symptom mitigation strategy had a higher prevalence of negative sentiments for anxiety and depression. Our proposed ML/NLP-based approach to collecting patient feedback from publicly available forums complements standard qualitative methods by enabling the scalable extraction of insights from hundreds of thousands of online conversations that could span a broad diversity of symptoms and well-being reflections. Furthermore, our findings suggest the potential value of informing clinical outcome assessment (COA) measurement for clinical trials, alone and in combination with standard qualitative research approaches to guide the next clinical development. Overall, our study highlights the significant potential of NLP and ML to address the critical need to better understand the eBC patient experience of disease and treatment to improve outcomes for patients. Citation Format: Sameet Sreenivasan, Chao Fang, Emuella Flood, Natasha Markuzon, Jasmine Sze. Analyzing Patient Experiences with Adjuvant Hormone Therapy for Early Breast Cancer Treatment via NLP/ML on Social Media: Symptoms and Their Mitigation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-12-07.

Leveraging sentiment analysis of Arabic Tweets for the 2022 FIFA World Cup insights, incorporating the gulf region

ABSTRACT Purpose The study analyzes Arabic Tweets, with a focus on those originating from the Gulf Cooperation Council (GCC) region, to examine public perceptions of the 2022 FIFA World Cup. Methodology Twitter’s search API was utilized to gather a substantial dataset of 157,756 tweets collected from 1 October 2022, to 1 January 2022. After data cleaning, a refined dataset of 43,658 tweets, was categorized into positive, neutral, and negative sentiments based on sentiment scores. Findings Arabic tweets, initially held a positive sentiment towards the World Cup, which increased during the tournament, accompanied by a decrease in negative sentiment. Research contribution This study offers valuable insights into public sentiment regarding the 2022 FIFA World Cup among Arabic-speaking Twitter users, with a particular emphasis on those from the GCC region. Additionally, it lends support to FIFA's decision to host the event in the Arab World by highlighting the favorable public reception. Originality This research's originality lies in its examination of the transformation in public sentiment within the Arabic-speaking community throughout the 2022 FIFA World Cup. These insights hold the potential to inform future decisions related to the hosting of major sporting events, providing a unique perspective on the evolving dynamics of sentiment within this demographic.

MAPPING OF SENTIMENT ANALYSIS ON FOOD SECURITY AMONG MALAYSIANS USING SOCIAL MEDIA ANALYTICS

The study investigates the spatial distribution of sentiment in food securityrelated tweets among Malaysians, aiming to identify hotspots of positive, negative, and neutral sentiments. Note that data collection involved gathering 2,218 tweets on basic necessity products from January 2019 to August 2022, capturing the impact of the COVID-19 pandemic. Pre-processing and content analysis techniques identified the main themes in the tweets, while sentiment analysis categorised them using Valance Aware Dictionary and Sentiment Reasoner (VADER). ArcGIS and hotspot analysis visualised and analysed tweet density. Regional trends were observed, with the East Coast states and certain West Coast states having the highest number of tweets. Urban areas expressed concerns about food security and living costs due to urbanisation and limited agricultural land. Here, sentiment visualisation revealed negative sentiments prevalent in developed cities (46.8%) and positive (33.9%) or neutral (19.4%) sentiments in suburban areas. This study emphasises the growing use of social media platforms and the integration of social media data in spatial analysis, highlighting the importance of uncovering hidden patterns for increased social awareness and meaningful dialogue. Understanding the sentiments expressed by Malaysians on social media regarding rising food prices can provide valuable insights for policymakers and stakeholders to address these challenges effectively.

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

Sentiment analysis is an essential task in natural language processing that involves identifying a text’s polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM.

Feeling analysis on allergen immunotherapy on Twitter using an unsupervised machine learning model

Analyze feelings about allergen-specific immunotherapy on Twitter using the VADER model VADER (Valence Aware Dictionary and sEntiment Reasoner) model. tweets related to specific allergen immunotherapy were obtained through the Twitter Application Programming Interface (API). The keywords "allergy shot" were used between January 1, 2012, and December 31, 2022. The data was processed by removing URLs, usernames, hashtags, multiple spaces, and duplicate tweets. Subsequently, a sentiment analysis was performed using the VADER model. A total of 34,711 tweets were retrieved, of which 1928 were eliminated. Of the remaining 32,783 tweets, 32.41% expressed a negative sentiment, 31.11% expressed a neutral sentiment, and 36.47% expressed a positive sentiment, with an average polarity of 0.02751 (neutral) over the 11-year period. The average polarity of tweets about allergen-specific immunotherapy is neutral over the 11 years analyzed. There was an annual increase in the average polarity over the years, with 2017, 2018, and 2022 having positive polarity averages. Additionally, the number of tweets decreased over time.

Analysis of Non-Muslim Consumer Sentiment Towards Halal Logo Products: A Case in Indonesia

This study aims to find out how non-Muslim consumers in Manado city view products with the halal logo. The method used is a qualitative approach combined with descriptive statistics, namely data collection through a Google form questionnaire which is distributed to non-Muslim communities living in Manado city, then processed with the SentiStrenght software. The results of this study indicate that non-Muslim consumers in Manado have positive sentiments towards the labeling and quality of halal products, as well as neutral sentiments towards the correlation between halal products and religious issues. This is because products that have a halal logo signify the authenticity of the product and meet halal requirements and have good quality. So if halal food can be aligned with the perceptions of non-Muslim consumers, then there will be opportunities to further expand the halal food market for non-Muslim consumers. Thus, this can provide an opportunity for non-Muslim consumers that halal food has universal values, that is, it can be consumed by anyone who needs it.

Sentiment Analysis of Twitter Discourse on the 2023 Nigerian General Elections

Sentiment analysis entails discerning whether text conveys positive, neutral, or negative sentiments to ascertain the mood of the public concerning a given entity. This method relies on natural language processing, computational linguistics, and text analysis to identify, extract, and methodically analyze affective and subjective data. The 2023 Nigerian presidential election holds immense significance for the nation, determining its leadership for the subsequent four years. Consequently, comprehending public sentiment regarding the electoral process becomes paramount. This study sought to gauge public sentiment concerning the 2023 Nigerian General Elections by analyzing tweets related to candidates and their political parties. Leveraging three machine learning (ML) techniques—SVM, RF, and XGBoost—we aimed to categorize tweets as negative, positive, or neutral. Our dataset comprised a substantial volume of tweets, meticulously pre-processed to eliminate irrelevant content and noise. Results showcased the outstanding performance of RF and XGBoost in tweets classification and sentiment identification about the electoral process with the highest accuracy (93%) and precision (96%), occurring on neutral opinions. These results findings offer crucial insights into public opinion regarding candidates, their political parties, and the electoral procedure, benefiting researchers, political analysts and decision-makers alike. It suggests that 43% of the electorate expressed neutral sentiments about the elections, while 33% expressed positive sentiments, such as optimism about the electoral process, support for specific candidates, satisfaction with the results of the election, or excitement for taking part in democracy. Meanwhile, 24% of the electorate expressed negative sentiments, such as dissatisfaction with political candidates, criticism of the electoral processes, worries about fairness, or skepticism about the outcome. This research underscores the significance of sentiment analysis in comprehending public opinion and its potential contributions to political discourse.

Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools

INTRODUCTION: Anti-vaccination agitation is on the rise, both in-person and online, notably on social media. The Internet has become the principal source of health-related information and vaccines for an increasing number of individuals. This is worrisome since, on social media, any comment, whether from a medical practitioner or a layperson, has the same weight. As a result, low-quality data may have a growing influence on vaccination decisions for children.
 OBJECTIVES: This paper will evaluate the scale and type of vaccine-related disinformation, the main purpose was to discover what caused vaccine fear and anti-vaccination attitudes among social media users.
 METHODS: The vaccination-related data used in this paper was gathered from Reddit, an information-sharing social media network with about 430 million members, to examine popular attitudes toward the vaccine. The materials were then pre-processed. External links, punctuation, and bracketed information were the first things to go. All text was also converted to lowercase. This was followed by a check for missing data. This paper is novel and different as Matplotlib, pandas, and word cloud was used to create word clouds and every result has a visual representation. The Sentiment analysis was conducted using the NLTK library as well as polarity and subjectivity graphs were generated.
 RESULTS: It was discovered that the majority population had neutral sentiments regarding vaccination. Data visualization methods such as bar charts showed that neutral sentiment outnumbers both positive and negative sentiment.
 CONCLUSION: Prevalent Sentiment has a big influence on how people react to the media and what they say, especially as people utilize social media platforms more and more. Slight disinformation and/or indoctrination can quickly turn a neutral opinion into a negative one.

What can TikTok tell us about the food practices of the residents of tiny apartments?

With continuing population growth and increased urbanisation, the prevalence of apartment living will also increase. The food practices and diet of apartment residents may differ to those in lower density, detached or semi-detached housing. Food practices may be impacted by physical constraints of apartment kitchens (size, storage, cooking facilities) and the influence of the surrounding community food environment, particularly in smaller apartments. High-rise apartment residents spend a larger proportion of their weekly household budget on eating foods prepared out of the home 1. This is concerning given meals prepared outside the home tend to be of lower diet quality 2 and are associated with higher body weight 3. This study investigated how kitchens, residents’ food practices and hypothesised barriers to healthy eating were portrayed on popular TikTok videos associated with hashtags denoting small apartments. Using the keyword ‘microapartment’, the top four most viewed hashtags relevant to the topic were identified: #studioapartment (190.5 million views), #smallapartment (152.5 million views), #tinyapartment (62 million views), and #microapartment/ #microapartments (4.9 million views combined). The most liked videos (n = 50) from each of these four hashtags and #apartment (as a comparator) were selected for investigation. Using a REDCap survey, two researchers independently coded a random sample of 15 videos, with comparison and discussion to tighten codebook definitions and reduce ambiguity. Subsequently over half of all videos were coded by at least two researchers, achieving acceptable inter-rater reliability (Cohen’s kappa >0.8). Each video was coded for engagement characteristics, user profile, and characteristics of individuals appearing. Further coding included apartment size, kitchen features displayed or mentioned, and food practices (grocery shopping, cooking at home, and eating foods prepared out of the home). Videos were categorised as having positive sentiment if they depicted the apartment in an explicitly positive way, including promoting or encouraging an aspect of apartment living or indicating support for a behaviour. Conversely, videos were coded as being of negative sentiment where they depicted a clear negative position on an aspect of apartment living. Else, the video was coded as neutral sentiment. The majority of videos (87%) portrayed apartment living with a positive or neutral sentiment; with only 2% of videos portraying kitchen size or function negatively. The expected physical constraints of small apartment kitchens were not evident in the videos, nor were interactions with the food environment surrounding the apartment. Indeed, in the small number of videos portraying the food practices of cooking, shopping and eating, the videos highlighted the ability to undertake these practices despite limitations of size and facilities. As the food practices of residents of small apartments are not well researched, it is not known whether their portrayal on TikTok may indeed reflect reality, or may be a glamorisation.

Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup

Football as an attractive sport generates huge volumes of tweets concerning fans' opinions, feelings, and judgments during prime events. Such data can be leveraged in sentiment analysis, an algorithmic approach to analyzing text in tweets by extracting emotional tones. This paper presents a novel benchmark dataset of 132,115 tweets collected during the 2022 world cup on 𝕏 (formerly Twitter) for football-related sentiment classification. We also performed sentiment analysis on the dataset using lexicon-based tools, traditional machine learning algorithms, and pre-trained models, robustly optimized bidirectional encoder representations from transformers (BERT)- pretraining approach RoBERTa and distilled version of BERT (DistilBERT) to understand the emotions and reactions of football fans during different phases of the football matches. Results from the study indicate that most tweets had neutral sentiments in both context-aware and context-free analysis. We also describe our novel GhaFootBERT, a sentiment classification model based on transfer learning on BERT, which provides an effective approach to sentiment classification of football-related tweets. Our model performs robustly, outperforming the traditional models with 92% accuracy.

What attributes affect customer satisfaction in green restaurants? An aspect-based sentiment analysis approach

ABSTRACT 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.