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Molenaar A, Jenkins EL, Brennan L, Lukose D, McCaffrey TA. The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review. Nutr Res Rev 2024; 37:43-78. [PMID: 36991525 DOI: 10.1017/s0954422423000069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Social media data are rapidly evolving and accessible, which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, thirty-six studies were selected from seventeen countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014 and 2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified: dietary patterns, cooking and recipes, diet and health, public health and nutrition and food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33·33% (open-source engine) to 98·53% (engine developed for the study). The average proportion of sentiment was 38·8% positive, 46·6% neutral and 28·0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.
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Affiliation(s)
- Annika Molenaar
- Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill, VIC3168, Australia
| | - Eva L Jenkins
- Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill, VIC3168, Australia
| | - Linda Brennan
- School of Media and Communication, RMIT University, 124 La Trobe St, MelbourneVIC3004, Australia
| | - Dickson Lukose
- Monash Data Futures Institute, Monash University, Level 2, 13 Rainforest Walk, Monash University, ClaytonVIC3800, Australia
| | - Tracy A McCaffrey
- Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill, VIC3168, Australia
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Abu-Salih B, Alhabashneh M, Zhu D, Awajan A, Alshamaileh Y, Al-Shboul B, Alshraideh M. Emotion detection of social data: APIs comparative study. Heliyon 2023; 9:e15926. [PMID: 37180895 PMCID: PMC10172785 DOI: 10.1016/j.heliyon.2023.e15926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/16/2023] Open
Abstract
The development of emotion detection technology has emerged as an efficient possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of various start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparisons to social data. This study compares eight technologies: IBM Watson Natural Language Understanding, ParallelDots, Symanto - Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and Natural Language Processing Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores they delivered and the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.
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Affiliation(s)
- Bilal Abu-Salih
- The University of Jordan, Amman, Jordan
- Curtin University, Perth, Australia
- Corresponding author. The University of Jordan, Amman, Jordan.
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Khalid ET, Salah Khalefa M, Yassen W, Adil Yassin A. Omicron virus emotions understanding system based on deep learning architecture. J Ambient Intell Humaniz Comput 2023; 14:9497-9507. [PMID: 37288131 PMCID: PMC10113983 DOI: 10.1007/s12652-023-04615-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 04/04/2023] [Indexed: 06/09/2023]
Abstract
Emotions understanding has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron virus as a case study to implement a emotions analysis framework to explore the global attitude and sentiment toward Omicron variant as an expression of Positive feeling, Neutral, and Negative feeling. Because since December 2021. Omicron variant has gained obvious attention and wide discussions on social media platforms that revealed lots of fears and anxiety feeling, due to its rapid spreading and infection ability between humans that could exceed the Delta variant infection. Therefore, this paper proposes to develop a framework utilizes techniques of natural languages processing (NLP) in deep learning methods using neural network model of Bidirectional-Long-Short-Term-Memory (Bi-LSTM) and deep neural network (DNN) to achieve accurate results. This study utilizes textual data collected and pulled from the Twitter platform (users' tweets) for the time interval from 11-Dec.-2021 to 18-Dec.-2021. Consequently, the overall achieved accuracy for the developed model is 0.946%. The produced results from carrying out the proposed framework for sentiment understanding have recorded Negative sentiment at 42.3%, Positive sentiment at 35.8%, and Neutral sentiment at 21.9% of overall extracted tweets. The acquired accuracy using data of validation for the deployed model is 0.946%.
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Affiliation(s)
- Eman Thabet Khalid
- Department of Computer Sciences, College of Education for Pure Sciences, University of Basrah, Basrah, 6100 Iraq
| | - Mustafa Salah Khalefa
- Department of Computer Sciences, College of Education for Pure Sciences, University of Basrah, Basrah, 6100 Iraq
| | - Wijdan Yassen
- Department of Computer Sciences, College of Education for Pure Sciences, University of Basrah, Basrah, 6100 Iraq
| | - Ali Adil Yassin
- Department of Computer Sciences, College of Education for Pure Sciences, University of Basrah, Basrah, 6100 Iraq
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Punetha N, Jain G. Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews. APPL INTELL 2023; 53:1-22. [PMID: 37363390 PMCID: PMC10063333 DOI: 10.1007/s10489-023-04471-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2023] [Indexed: 06/28/2023]
Abstract
Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.
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Affiliation(s)
- Neha Punetha
- Department of Applied Mathematics, Delhi Technological University, New Delhi, India
| | - Goonjan Jain
- Department of Applied Mathematics, Delhi Technological University, New Delhi, India
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Mishra E, Nikam P, Vidhyadharan S, Cheruvalath R. An affect-based approach to detect collective sentiments of film audience: Analyzing emotions and attentions. Acta Psychol (Amst) 2022; 230:103736. [PMID: 36115203 DOI: 10.1016/j.actpsy.2022.103736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 08/03/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Affective computing plays an important role in simulating human affects through multimedia stimuli. To provide appropriate responses and quantify emotions, it is essential to identify and interpret emotions accurately. Often the audience is influenced by media content and it has an impact on the audience's emotions. The authors aim to develop a machine vision based smart affective system that investigates the correlation between the basic facial expressions and corresponding ratings given by the audience. For the same, a notion of emotion vector is introduced to measure the elicited emotion. The idea of measuring attention is also suggested to find if the con- tent could keep the audience engaged. Our results reveal that such systems are useful in capturing and analyzing individual and collective sentiment.
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Venkateswarlu B, Shenoi VV, Tumuluru P. CAViaR-WS-based HAN: conditional autoregressive value at risk-water sailfish-based hierarchical attention network for emotion classification in COVID-19 text review data. Soc Netw Anal Min 2021; 12:10. [PMID: 34849175 PMCID: PMC8620331 DOI: 10.1007/s13278-021-00843-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 11/05/2021] [Accepted: 11/11/2021] [Indexed: 12/01/2022]
Abstract
The Corona Virus Disease-2019 (COVID-19) pandemic has made a remarkable impact on economies and societies worldwide. With numerous procedures of social distancing and lockdowns, it becomes essential to know people's emotional responses on a very large scale. Thus, an effective emotion classification approach is developed using the proposed Conditional Autoregressive Value at Risk-Water Sailfish-based Hierarchical Attention Network (CAViaR-WS-based HAN) for classifying the emotions in the COVID-19 text review data. The proposed approach, named CAViaR-WS, is designed by the incorporation of Conditional Autoregressive Value at Risk-Sail Fish (CAViaR-SF) and Water Cycle Algorithm (WCA). Here, the significant features, such as mean, variance, entropy, Term Frequency-Inverse Document Frequency (TF-IDF), SentiWordNet features, and spam word-based features, are extracted to further processing. Based on the extracted features, feature fusion is accomplished using the RideNN. In addition, CAViaR-SF-based GAN is used to perform the spam classification, and then, the emotion classification is carried out using Hierarchal Attention Networks (HAN), where the training procedure of HAN is performed using proposed CAViaR-WS. Furthermore, the developed CAViaR-WS-based HAN offers effective performance results concerning precision, recall, and f-measure with the maximal values of 0.937, 0.958, and 0.948, respectively.
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Affiliation(s)
- B Venkateswarlu
- Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh India
| | - V Viswanath Shenoi
- Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh India
| | - Praveen Tumuluru
- Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh India
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Cao G, Shen L, Evans R, Zhang Z, Bi Q, Huang W, Yao R, Zhang W. Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic. Comput Methods Programs Biomed 2021; 212:106468. [PMID: 34715513 PMCID: PMC8516441 DOI: 10.1016/j.cmpb.2021.106468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND With outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated. OBJECTIVES This study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020. METHODS Data related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized. RESULTS Seven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics. CONCLUSIONS Multiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.
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Affiliation(s)
- Guang Cao
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
| | - Lining Shen
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China; Hubei Provincial Research Center for Health Technology Assessment, Wuhan, China; Institute of Smart Health, Huazhong University of Science & Technology, Wuhan, China.
| | - Richard Evans
- College of Engineering, Design and Physical Sciences, Brunel University London, London, United Kingdom.
| | - Zhiguo Zhang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China; Hubei Provincial Research Center for Health Technology Assessment, Wuhan, China.
| | - Qiqing Bi
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
| | - Wenjing Huang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
| | - Rui Yao
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
| | - Wenli Zhang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
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8
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Wang N, Lv X, Sun S, Wang Q. Research on the effect of government media and users' emotional experience based on LSTM deep neural network. Neural Comput Appl 2021; 34:12505-12516. [PMID: 34642547 PMCID: PMC8494169 DOI: 10.1007/s00521-021-06567-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/20/2021] [Indexed: 11/25/2022]
Abstract
Different government media have different communication effects and users' emotional experience. It carries on a comparative research on government media selecting three different types of government media which include China’s Police Online, Central Committee of the Communist Youth League, and China’s Fire Control in the context of public health emergencies. Based on the deep learning technique, the emotion classification model of long-term memory network is constructed to analyze the emotion of the users’ comments of different government media; taking the number of contents, the number of retweets, the number of praises, and the number of comments as evaluating indicators to do comparative analysis to cross platform government medias. Through the comparative results, it is found that different types and platforms of government media have great differences in users’ emotional experience; the emotion performance of users’ comments is strongly related to the information communication power and effectiveness of government media.
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Affiliation(s)
- Nan Wang
- Jilin University of Finance and Economics, Changchun, 130117 China
| | - Xinlong Lv
- Jilin University of Finance and Economics, Changchun, 130117 China
| | - Shanwu Sun
- Jilin University of Finance and Economics, Changchun, 130117 China
| | - Qingjun Wang
- Shenyang Aerospace University, Shenyang, 110136 China
- Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China
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Abstract
Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.
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Affiliation(s)
- Keith Cortis
- ADAPT Centre, Dublin City University, Dublin, Ireland
| | - Brian Davis
- ADAPT Centre, Dublin City University, Dublin, Ireland
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Kabir MY, Madria S. EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Soc Netw Media 2021; 23:100135. [PMID: 34722957 PMCID: PMC8542648 DOI: 10.1016/j.osnem.2021.100135] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/26/2021] [Accepted: 04/03/2021] [Indexed: 11/30/2022]
Abstract
The adversarial impact of the Covid-19 pandemic has created a health crisis globally all over the world. This unprecedented crisis forced people to lockdown and changed almost every aspect of the regular activities of the people. Thus, the pandemic is also impacting everyone physically, mentally, and economically, and it, therefore, is paramount to analyze and understand emotional responses during the crisis affecting mental health. Negative emotional responses at fine-grained labels like anger and fear during the crisis might also lead to irreversible socio-economic damages. In this work, we develop a neural network model and train it using manually labeled data to detect various emotions at fine-grained labels in the Covid-19 tweets automatically. We present a manually labeled tweets dataset on COVID-19 emotional responses along with regular tweets data. We created a custom Q&A roBERTa model to extract phrases from the tweets that are primarily responsible for the corresponding emotions. None of the existing datasets and work currently provide the selected words or phrases denoting the reason for the corresponding emotions. Our classification model outperforms other systems and achieves a Jaccard score of 0.6475 with an accuracy of 0.8951. The custom RoBERTa Q&A model outperforms other models by achieving a Jaccard score of 0.7865. Further, we present a historical emotion analysis using COVID-19 tweets over the USA including each state level analysis.
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Affiliation(s)
- Md Yasin Kabir
- Department of Computer Science, Missouri University of Science and Technology, USA
| | - Sanjay Madria
- Department of Computer Science, Missouri University of Science and Technology, USA
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Choudrie J, Patil S, Kotecha K, Matta N, Pappas I. Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study. Inf Syst Front 2021; 23:1431-1465. [PMID: 34188606 PMCID: PMC8225489 DOI: 10.1007/s10796-021-10152-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/24/2021] [Indexed: 05/04/2023]
Abstract
The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.
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Affiliation(s)
- Jyoti Choudrie
- University of Hertfordshire, Hertfordshire Business School, Hatfield, Hertfordshire, AL10 9EU UK
| | - Shruti Patil
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, MH 412115 India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, MH 412115 India
| | - Nikhil Matta
- Symbiosis International University, Symbiosis Institute of Technology, Pune, India
| | - Ilias Pappas
- University of Agder: Universitetet i Agder, Kristiansand, Norway
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12
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Arora A, Chakraborty P, Bhatia MPS, Mittal P. Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India. J Technol Behav Sci 2020; 6:370-377. [PMID: 33102690 PMCID: PMC7572156 DOI: 10.1007/s41347-020-00174-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/26/2020] [Accepted: 10/09/2020] [Indexed: 01/07/2023]
Abstract
The COVID-19 pandemic and the lockdowns to contain it are affecting the daily life of people around the world. People are now using digital technologies, including social media, more than ever before. The objectives of this study were to analyze the social media usage pattern of people during the COVID-19 imposed lockdown and to understand the effects of emotion on the same. We scraped messages posted on Twitter by users from India expressing their emotion or view on the pandemic during the first 40 days of the lockdown. We identified the users who posted frequently and analyzed their usage pattern and their overall emotion during the study period based on their tweets. It was observed that 222 users tweeted frequently during the study period. Out of them, 13.5% were found to be addicted to Twitter and posted 13.67 tweets daily on an average (SD: 4.89), while 3.2% were found to be highly addicted and posted 40.71 tweets daily on an average (SD: 9.90) during the study period. The overall emotion of 40.1% of the users was happiness throughout the study period. However, it was also observed that users who tweeted more frequently were typically angry, disgusted, or sad about the prevailing situation. We concluded that people with a negative sentiment are more susceptible to addictive use of social media.
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Affiliation(s)
- Anshika Arora
- Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, 110078 India
| | - Pinaki Chakraborty
- Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, 110078 India
| | - M. P. S. Bhatia
- Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, 110078 India
| | - Prabhat Mittal
- Department of Commerce, Satyawati College (Evening), University of Delhi, Delhi, 110052 India
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Tsakalidis A, Papadopoulos S, Voskaki R, Ioannidou K, Boididou C, Cristea AI, Liakata M, Kompatsiaris Y. Building and evaluating resources for sentiment analysis in the Greek language. LANG RESOUR EVAL 2018; 52:1021-1044. [PMID: 30930705 PMCID: PMC6411313 DOI: 10.1007/s10579-018-9420-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language. We present and make publicly available a rich set of such resources, ranging from a manually annotated lexicon, to semi-supervised word embedding vectors and annotated datasets for different tasks. Our experiments using different algorithms and parameters on our resources show promising results over standard baselines; on average, we achieve a 24.9% relative improvement in F-score on the cross-domain sentiment analysis task when training the same algorithms with our resources, compared to training them on more traditional feature sources, such as n-grams. Importantly, while our resources were built with the primary focus on the cross-domain sentiment analysis task, they also show promising results in related tasks, such as emotion analysis and sarcasm detection.
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Affiliation(s)
- Adam Tsakalidis
- 1Department of Computer Science, University of Warwick, Coventry, UK.,The Alan Turing Institute, London, UK
| | | | | | - Kyriaki Ioannidou
- 4Laboratory of Translation and Natural Language Processing, Aristotle University of Thessaloniki, Thessaloníki, Greece
| | | | - Alexandra I Cristea
- 1Department of Computer Science, University of Warwick, Coventry, UK.,6Department of Computer Science, University of Durham, Durham, UK
| | - Maria Liakata
- 1Department of Computer Science, University of Warwick, Coventry, UK.,The Alan Turing Institute, London, UK
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Poria S, Cambria E, Hussain A, Huang GB. Towards an intelligent framework for multimodal affective data analysis. Neural Netw 2014; 63:104-16. [PMID: 25523041 DOI: 10.1016/j.neunet.2014.10.005] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2013] [Revised: 09/19/2014] [Accepted: 10/09/2014] [Indexed: 10/24/2022]
Abstract
An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.
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Affiliation(s)
| | - Erik Cambria
- School of Computer Engineering, Nanyang Technological University, Singapore.
| | - Amir Hussain
- School of Natural Sciences, University of Stirling, UK.
| | - Guang-Bin Huang
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore.
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