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Analysis of sample of 20 tweets by TextBlob and RNN, using 'covid' keyword. (a) TextBlob result, (b) RNN result.
Source publication
In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media pl...
Contexts in source publication
Context 1
... third-party models will not be mentioned in detail, as an analyst based on a simple dictionary has already given completely misleading results on tweets that have reported positive or negative disease of the virus outcomes on a given topic. Like (Figure 2), the textBlob and our own well-trained model were able to filter out these word turns and manifestations really accurately. (Maybe, the RNN looks more significant, but now, we cannot prove it 100%, but the RNN has not have a Neutral section most of the time, which gives us more improvement to the analysis.) ...
Context 2
... the categorization of both models can be realistic, the difference is to be found primarily in the detail handling of the models, which hopefully our model handled better even with so little test data. Figure 2 worked from this DataSet (Figure 3). We continue to compare TextBlob and our own RNN model, how it performs on larger and larger test datasets, and how accurate it gives less erroneous results, with double denials and other, 'sleng' and general manifestations, reports. ...
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Citations
... Melalui pendekatan ini menganalisis sentimen masyarakat dengan pendekatan machine learning akan melalui empat proses yaitu pengumpulan data, pra pemrosesan data, analisis dan evaluasi penelitian [5], kita bisa mengetahui informasi lebih lanjut terkait bagaimana berbagai pihak menerima atau menolak perubahan ini, serta faktor apa yang mendasari persepsi mereka. Dengan perkembangan teknologi menganalisis sentimen dapat menggunakan pendekatan machine learning dimana ini bisa dikategorikan dalam natural language processing yaitu suatu proses mengambil dan mengolah data opini masyarakat melalui berbagai media sosial salah satunya adalah media sosial X merupakan suatu hal yang menarik dalam hal mengidentifikasi dan memahami perasaan seseorang terhadap suatu permasalahan maupun fenomena dalam bentuk membuat suatu tweet dalam media sosial X dimana natural language processing sudah banyak digunakan dalam penelitian berbagai bidang [6], [7], [8], [9]. ...
social media X or formerly more familiar with Twitter is one of the familiar social media and has many users in the world whis is a platform for accesing some information and commenting both suggestions and criticsm related to the development of the Capital City of the Archipelago (IKN) which is the center of smart government in East Kalimantan. There are indormation, suggestions and criticisms addressed to the @ikn_id account directly addressed to the Indonesia government as well as public opinions related to IKN by using the IKN hashtag. Public sentiment on the issue is in the form of text on IKN Development. This research aims to analyze public opinion on the government's decision to build the Capital City of Nusatara (IKN) conveyed through X social media using appropriate data analysis methods by comparing the performance of support vector machine, logistic regression, and naïve bayes algorithms and identifying the most effective algorithm in sentiment analysis. The method used in this research to analyze sentiment are support vector machine, logistic regression and naïve bayes. The use of these three algorithms is also to compare the accuracy that is better than other algorithms. The results obtained using the Support Vector Machine algorithm is 80% while using the Logistic Regression and naïve bayes algorithms are 79%.
... These effects include symptoms of post-traumatic stress disorder, long quarantine suppress symptoms, and many others. Social media outlets like Facebook and Twitter, news platforms, blogs, and forums, during the periods of the pandemic have been experiencing reactions, feelings, emotions, and thoughts of people towards the COVID-19 pandemic and its impacts [10]. ...
The world health organization (WHO) has officially declared the COVID-19 as a global pandemic in March 2020. Consequently, nations worldwide took some preventive measures, including lockdowns, quarantines, and social distancing to slow down the spread of coronavirus. This unprecedented event has profoundly disrupted the normal way of life. The pandemic had devastating impacts on various aspects of society such as healthcare systems, social life, the economy, and education. People from around the world began expressing emotions of fear, isolation, and various kinds of traumatic disorders on social media networks such as Twitter and Facebook. This research paper explores the impacts of COVID-19 in Morocco using topic modeling, sentiment analysis, and time series analysis. The study follows a two-step process. Initially, we employed a topic model, specifically BERTopic, to extract the main themes from a dataset containing comments gathered from the online newspaper Hespress and Twitter. Subsequently, we conducted a topic-based sentiment analysis to assess how COVID-19 has impacted Moroccans through a time window of three years. The findings revealed that sentiments related to the various topics were highly negative. In addition, we leveraged time-series data on COVID-19 to examine how the evolving epidemiological situation influenced sentiments from March 2020, the beginning of the pandemic, until the end of 2022. Our analysis indicated a strong correlation between changes in COVID-19 cases and sentiment analysis results.
... Nemes and Kiss [13] proposed a recurrent neural network (RNN) to categorize the emotions on Twitter data. Tey designed a model to examine the emotions and feelings of diverse tweets, by using a RNN for sentimental prediction and labeled them with weakly positive/negative, and strongly positive/negative emotions. ...
... For sentiment prediction, Nemes and Kiss [13] used the RNN, and Imran et al. [14] investigated sentiment across neighboring countries during the pandemic. Chakraborty et al. [15] and Basiri et al. [16] used SVM and hybrid model for the sentiment analysis, while Kaur and Ranjan [17] and Gupta et al. [18] studied public reactions to lockdowns and pandemics. ...
... Chakraborty et al. [15] and Basiri et al. [16] used SVM and hybrid model for the sentiment analysis, while Kaur and Ranjan [17] and Gupta et al. [18] studied public reactions to lockdowns and pandemics. In emotion detection and sentiment analysis, Nemes and Kiss [13] used the RNN for sentiment prediction and compared it with TextBlob. Te study found that TextBlob has a signifcant portion of neutral sentiments which limit its efectiveness in emotion detection. ...
COVID-19 has significantly impacted peoples’ mental health because of isolation and social distancing measures. It practically impacts every segment of people’s daily lives and causes a medical problem that spreads throughout the entire world. This pandemic has caused an increased emotional distress. Since everyone has been affected by the epidemic physically, emotionally, and financially, it is crucial to examine and comprehend emotional reactions as the crisis affects mental health. This study uses Twitter data to understand what people feel during the pandemic. We collected Twitter data about COVID-19 and isolation, preprocessed the text, and then classified the tweets into various emotion classes. The data are collected using the twarc library and the Twitter academic researcher account and labeled using a Vader analyzer after preprocessing. We trained five machine learning models, namely, support vector machine (SVM), Naïve Bayes, KNN, decision tree, and logistic regression to find patterns and trends in emotions. The emotional reactions of individuals to the COVID-19 crisis are then analyzed. We applied precision, recall, F1-score, and accuracy as the evaluation metrics, which shows that SVM has performed best among other models. Our results show that isolated people felt various emotions, out of which, fear, sadness, and surprise were the most common. This study gives insights into the emotional impact of the pandemic and shows the power of Twitter data in understanding mental health outcomes. Our findings can be used to develop targeted interventions and support strategies to address the emotional toll of the pandemic.
... Studies in this field offer insights into social impacts and phenomena on these networks, as evidenced by studies like [2,24]. One prevalent approach is sentiment analysis through various strategies, ranging from simplified methods like the influential study by [14] to more advanced neural network techniques [23,25]. These studies aim to computationally classify the emotional content of sentences, a complex task with numerous applications [8,9]. ...
Multimedia communication has become an essential part of social media, with images representing a significant part of the content on most platforms. This study investigates image content on Instagram through Meta’s internal image classification algorithm, Automatic Alt-Text (AAT). Our approach differs from research on data from comments and hashtags because of the use of actual visual descriptions as the means of understanding the kinds of the content published on the network. Our analysis of 200k posts reveals 1,471 unique tags being used to characterize image content on Instagram, representing mostly objects, food, animals, locations and other common components of social media photos. Notably, we found that content about personal aesthetics is highly popular on the platform, with person and selfie being respectively some of the top two most common tag and post categories, being also highly related to other tags such as makeup, lipstick and eyeliner. Furthermore, we explored the connections between tags, representing very popular content trends within the network. Finally, we uncover substantial differences in posting behavior of influencers and news pages when compared to regular users, observing they post more frequently and about more specific content, suggesting what may attract more engagement on Instagram.
... The role of social media in understanding health information needs Social media platforms have become invaluable sources of information and support for individuals facing health challenges (25)(26)(27). For instance, platforms like Twitter and Facebook have been widely used to study public health trends, such as the spread of infectious diseases (e.g., COVID-19) (28)(29)(30), mental health conditions, and chronic illnesses like diabetes and heart disease. Studies have shown that social media allows for real-time data collection, capturing immediate public reactions and concerns, which traditional methods may not achieve as efficiently (27,31). ...
Objective
This study explores the health information needs of individuals with autism spectrum disorder (ASD) and their caregivers in the post-COVID-19 era by analyzing discussions from Reddit, a popular social media platform.
Methods
Utilizing a mixed-method approach that integrates qualitative content analysis with quantitative sentiment analysis, we analyzed user-generated content from the “r/autism” subreddit to identify recurring themes and sentiments.
Results
The qualitative analysis uncovered key themes, including symptoms, diagnostic challenges, caregiver experiences, treatment options, and stigma, reflecting the diverse concerns within the ASD community. The quantitative sentiment analysis revealed a predominance of positive sentiment across discussions, although significant instances of neutral and negative sentiments were also present, indicating varied experiences and perspectives among community members. Among the machine learning models used for sentiment classification, the Bi-directional Long Short-Term Memory (Bi-LSTM) model achieved the highest performance, demonstrating a validation accuracy of 95.74%.
Conclusions
The findings highlight the need for improved digital platforms and community resources to address the specific health information needs of the ASD community, particularly in enhancing access to reliable information and fostering supportive environments. These insights can guide future interventions and policies aimed at improving the well-being of autistic persons and their caregivers.
... With the help of the TextBlob Python library, we conducted a sentiment analysis of influencer statements to gain insight into the emotional tone and impact. Each statement was classified as positive, negative or neutral based on the sentiment expressed (Kaur & Sharma, 2020;Nemes & Kiss, 2021). This analysis provided a high-level understanding of the overall sentiment landscape within the influencer marketing domain. ...
As Artificial Intelligence and machine learning (ML) permeate diverse aspects of daily life, a new category of 'consumer mimic technology' has emerged, promising personalised and efficient user experiences. The purpose of this chapter is to gain a better understanding of consumer mimic technology in relation to influencer marketing through a robust methodology that combines qualitative and quantitative analysis and discusses its applications , impacts and ethical ramifications. The analysis reveals how mimic technology shapes consumer behaviour via highly tailored influencer content. Findings suggest these systems effectively drive engagement and purchasing decisions by leveraging emotional connections and projecting credibility. However, the extensive tracking of personal data to enable mimic algorithms frequently disregards user privacy and transparency. As this chapter highlights, personalisation must be balanced with ethical boundaries, and mimicry technology should be developed in a manner that empowers consumers while avoiding harm to them. At the intersection of marketing, consumer behaviour, ML and privacy, this work elucidates the promise and peril of this rapidly evolving field. With the advancement of these technologies , further research can help promote ethical innovation.
... This article [23] focuses on analyzing the sentiments and expressions (comments, hashtags, posts, tweets) of Twitter users within the context of COVID-19 and the coronavirus theme. The analysis is conducted using Natural Language Processing techniques and Sentiment Classification, specifically employing a Recurrent Neural Network. ...
COVID-19 has significantly impacted individuals, communities, and countries worldwide. These effects include health impacts, economics impacts, social impacts, educational, political and environmental impacts. The COVID-19 vaccine development was crucial for disease control and monitoring, yet the threat still looms large. Vaccine recommender systems can help the health practitioners in combating COVID-19 by providing the information and guidance on the benefits and risks of COVID-19 vaccines to individuals based on their preferences and medical history. In this paper, we have proposed sentiment analysis based recommender system for COVID-19 vaccines. We used Twitter data of 10,000 tweets about COVID-19 vaccines and applied pre-processing steps. We propose an ensemble of random forest with CT-BERT_CONVLayerFusion model, a novel algorithm, for classifying the tweets into seven different categories of sentiments. We also performed aspect-based review categorization which works on the queries given by a user. We compared the results of sentiment classification with the state-of-the-art with metrics including accuracy, recall, precision, and F1-score, and found out that our proposed approach outperformed all other state-of-the-art model by achieving maximum accuracy, recall, precision and F1-measure. Hence, such advanced methods can help somehow to fight COVID-19 as well as reducing the vaccine hesitancy by suggesting proper vaccines to patients based on the their specific concerns and questions.
... Since our study was based on scraping publicly available data on social media, demographics were not available to us, a common limitation in social media research (e.g. Nemes & Kiss, 2021). ...
The Metaverse is gaining attention as a potential future workplace, and advancements in VR/AR technologies are set to revolutionise how we work and collaborate. Extensive research using big data is still needed to fully comprehend the public’s perception of this emerging field. Grounded in the Technology Acceptance Model (TAM), the Diffusion of Innovations Theory (DIT), and Social Presence Theory (SPT), this study seeks to fill this knowledge gap. Using a methodology that involved machine learning and qualitative analysis of big data, the research gathered comments from social media users on widely viewed YouTube videos discussing the Metaverse workplace. The initial dataset, which contained 6,982 comments, underwent thorough cleaning processes, resulting in the analysis of 2,804 comments through thematic, emotion, and sentiment analyses. The process of the thematic analysis revealed that out of the total comments, 472 were unclassified, whilst the remaining 2,332 helped structure the public’s beliefs about the Metaverse workplace into four overarching themes: 1- benefits of flexibility and accessibility (37%), highlighting VR’s potential to transform workspaces, especially for creative fields and efficient space use; 2- Health concerns (26%), including eye strain and physical discomfort from prolonged headset use; 3- data privacy and corporate control fears (20%), reflecting worries over pervasive data collection and potential misuse of power; 4- scepticism over readiness and practicality (17%), noting visual clarity challenges and ergonomic issues. The overall vibes about working in the Metaverse are mixed. While more than half the sentiments were positive, expressing contentment, curiosity and enthusiasm, there were also concerns about health effects, data privacy, and integration issues. The public recognises Metaverse’s potential for remote work, desiring improvements in areas like visual clarity, ergonomics and productivity support before widespread adoption. This study is a pioneering effort in the field, providing a first-of-its-kind structure of the public’s beliefs about the Metaverse workplace, drawing upon naturally occurring data. The findings not only contribute to the academic understanding of the Metaverse workplace but also have significant implications for society and practitioners for optimising the positive aspects to enhance overall acceptance in this relatively understudied field.
Keywords. Metaverse workplace, digital transformation, big data, public’s beliefs and attitudes.
... In recent years, social media content analysis research has surged, reflecting the growing importance of these platforms in communication and information sharing. Topics explored include Emotion [9] and sentiment analysis [10], Trend Prediction [11], fake news detection [12], misinformation propagation [13], user profiling, gender identification [14], and community detection. Studies aim to understand emotions in posts and combat deceptive narratives. ...
The proliferation of social media platforms, such as Facebook, has led to an exponential increase in diverse user-generated content, including text, images, and videos that could be pivotal in public health contexts. This research paper presents an innovative approach to summarizing Facebook posts that incorporate multiple modalities, aiming to generate concise and informative summaries. Utilizing the MT5 architecture for textual analysis and state-of-the-art computer vision for image and video processing, our approach achieves a seamless integration of diverse modalities. Rigorously evaluated against a comprehensive suite of metrics, including ROUGE and BLEU, our model demonstrates notable effectiveness, with GPT-4 achieving impressive scores across various metrics (BLEU-1: 0.82, BLEU-2: 0.68, BLEU-3: 0.59, ROUGE-1: 0.78, ROUGE-2: 0.60, ROUGE-L: 0.73, METEOR: 0.58, CIDEr: 1.75, SPICE: 0.37). These results highlight our methodology's capability to produce succinct and informative summaries, significantly enhancing decision-making processes in public health monitoring and response. This achievement not only underscores the potential of advanced models in handling the complexity of multi-modal content but also sets a new benchmark for future explorations in social media analytics and user experience enhancement.
... In addition to demonstrating the effectiveness of sentiment analysis, previous studies [5,6,7] on COVID-19 analysis in languages other than Bengali also advocate the use of both conventional ML algorithms and deep learning (DL) models for sentiment analysis tasks. Among these studies, DL-based approaches are commonly utilized for sentiment classification. ...
This study examines the impact of the COVID-19 epidemic on students in Bangladesh through text classification using various machine learning (ML) algorithms and deep learning (DL) models. The pandemic led to emergency crisis protocols in the country, including self-quarantine and the closure of educational and governmental institutions, resulting in significant negative impacts on individuals’ physical and mental health, including anxiety, sadness, and terror. To better understand the psychological effects of the epidemic, the authors collected survey data from 400 students in various divisions of Bangladesh using self-administered questionnaires through Google Forms. Preprocessing techniques such as tokenization, filtering, and n-gram modeling were used in the analysis. The study deployed eight different ML algorithms and DL models, including LSTM, BiLSTM, and CNN, to classify the effects on students’ academic, mental, and social lives. The results show that the ML classifier algorithms were highly effective, achieving accuracies of 95.00%, 93.75%, and 95.00% for academic, mental, and social life impact, respectively. Furthermore, hybrid DL models, such as CNN-LSTM and CNN-BiLSTM, produced good scores in predicting the impacts on students’ lives. Overall, this study provides valuable insights into the impacts of the COVID-19 epidemic on students’ academic, mental, and social well-being in Bangladesh.
