Public Perception of the Use of Digital Contact-Tracing Tools After the COVID-19 Lockdown: Sentiment Analysis and Opinion Mining

Huang, Zhilian; Tay, Evonne; Wee, Dillon; Guo, Huiling; Lim, Hannah Y.; Chow, Angela

doi:10.2196/33314

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…For instance, in Ref. [44], the authors analysed emotions from the data obtained from the TraceTogether app and conducted a cross-sectional survey at the large public hospital in Singapore after the COVID-19 lockdown.…”

Section: Speech Emotion Recognition (Ser) and Covid-19mentioning

confidence: 99%

Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic

Pucci

Fedele²,

Dimitri

2023

Cognitive Comp and Systems

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If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER.

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Section: Speech Emotion Recognition (Ser) and Covid-19mentioning

confidence: 99%

Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic

Pucci

Fedele²,

Dimitri

2023

Cognitive Comp and Systems

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“…These insights can inform the development and implementation of similar technologies, promoting effective communication and addressing public concerns in the future. [2] Pelaez (2020) wrote that, In the past decade, the advertising industry has undergone significant advancements in neuroscience, artificial intelligence, and consumer expertise, leading to a greater focus on opinion mining, sentiment analysis, and emotion understanding. These advancements aim to achieve a key advertising objective: delivering relevant advertisements on a large scale.…”

Section: Fig I Sentiment Analysis In Online Product Reviewsmentioning

confidence: 99%

Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification

2023

IJFMR

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Online product reviews have become a valuable resource for consumers seeking detailed information and making informed choices. The process of automatically extracting sentiment or opinions from these reviews heavily relies on sentiment analysis, a branch of Natural Language Processing (NLP). This research article focuses on sentiment categorization in online product evaluations, utilizing innovative techniques for mining consumer opinions. The project aims to establish a robust framework for sentiment analysis that accurately classifies emotions expressed in these reviews. The proposed system incorporates advanced deep learning and machine learning methods to enhance data classification and extract fine-grained sentiment information. The study addresses the unique challenges of sentiment analysis in the context of online product evaluations, including polarity changes, sarcasm, and domain-specific sentiment expressions, which often pose significant obstacles to precise sentiment classification. The approach combines feature engineering and deep learning techniques, extracting lexical, syntactic, and semantic features such as part-of-speech tags, n-grams, sentiment lexicons, and word embeddings from the review texts. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed as sophisticated neural network architectures to leverage these features, creating robust representations and capturing contextual information. The suggested architecture is extensively evaluated on a large dataset of online product reviews, demonstrating superior performance in sentiment categorization compared to existing approaches. The evaluation encompasses various sentiment classes, measuring metrics like accuracy, recall, and F1-score, and assessing the framework's adaptability to different product domains. The study showcases the effectiveness of advanced machine learning and deep learning algorithms in sentiment categorization, advancing the field of sentiment analysis for online product evaluations. Businesses can gain valuable insights into customer sentiment and make well-informed decisions regarding product enhancements and marketing strategies by leveraging the proposed framework

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“…According to the study of Pelucio et al [5], which conducted in one of the universities in Brazil for evaluating the presence of depression, and anxiety in university students during Covid-19 pandemic, the results revealed that most of the students reported emotional impact with significant difference of depressive symptoms but no significant difference in anxiety. However, the computer scientist and psychologist can work together in detecting the students feeling by using the opinion mining or the sentiment analyses to study their attitudes and emotions [6]. Therefore, in this study the sentiment analysis of students will be detected and evaluated by developing a chatbot based on supervised learning algorithms, however the corpus in this study is English tweets which classified as positive or negative.…”

Section: Introductionmentioning

confidence: 99%

Psychological Emotion Recognition of Students using Machine Learning based Chatbot

Assayed¹,

Shaalan²,

Alsayed³

2023

IJAIA

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Anxiety and depression can have a significant impact on students’ academic performance, however, these mental health impacts were increased during the Covid-19 pandemic, and accordingly students and parents need some people to share their feelings together; however, there are different types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the most popular social application that people prefer to share their emotional states. Interestingly, the psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.

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Public Perception of the Use of Digital Contact-Tracing Tools After the COVID-19 Lockdown: Sentiment Analysis and Opinion Mining

Cited by 5 publications

References 27 publications

Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic

Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic

Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification

Psychological Emotion Recognition of Students using Machine Learning based Chatbot

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