Over a decade of social opinion mining: a systematic review

Cortis, Keith; Davis, Brian

doi:10.1007/s10462-021-10030-2

Cited by 63 publications

(40 citation statements)

References 559 publications

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“…NLP is a research and application area that explores the use of computers to understand and manipulate natural language from text or speech and, although it is not a recent area of study, it has been gaining more and more relevance in several fields [9,10]. NLP can be used to extract relevant, and in some cases, unknown information about characteristics or symptoms of some diseases, such as COVID-19 infections [11].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of COVID-19 Symptoms in Social Networks through Machine Learning

2022

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Social media is a great source of data for analyses, since they provide ways for people to share emotions, feelings, ideas, and even symptoms of diseases. By the end of 2019, a global pandemic alert was raised, relative to a virus that had a high contamination rate and could cause respiratory complications. To help identify those who may have the symptoms of this disease or to detect who is already infected, this paper analyzed the performance of eight machine learning algorithms (KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT) in the search and classification of tweets that mention self-report of COVID-19 symptoms. The dataset was labeled using a set of disease symptom keywords provided by the World Health Organization. The tests showed that Random Forest algorithm had the best results, closely followed by BERT and Convolution Neural Network, although traditional machine learning algorithms also have can also provide good results. This work could also aid in the selection of algorithms in the identification of diseases symptoms in social media content.

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Section: Introductionmentioning

confidence: 99%

Predictive Analysis of COVID-19 Symptoms in Social Networks through Machine Learning

2022

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“…Detection of aspects is a crucial step in the analysis of customers' reviews for products or services in e-commerce and social platforms as it helps product owners and service providers to find shortcomings and prioritize the improvements according to customers needs, Reviews: [latent] vs. explicit aspects -were given table far from river [management] -the girl [staff] at the front desk was really nice -they advertise free wifi but charge you for that -menu is extensive and a bar with live music and hence, maintaining revenues and decrease customer churn (Napitu et al, 2017). As such, aspect detection along with customers' opinion mining and sentiment analysis has long gained much attention (Cortis and Davis, 2021).…”

Section: Introductionmentioning

confidence: 99%

Latent Aspect Detection from Online Unsolicited Customer Reviews

Forouhesh¹,

Mansouri²,

Fani³

2022

Preprint

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Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs, and hence, maintain revenues and mitigate customer churn. Existing methods focus on detecting the surface form of an aspect by training supervised learning methods that fall short when aspects are latent in reviews. In this paper, we propose an unsupervised method to extract latent occurrences of aspects. Specifically, we assume that a customer undergoes a two-stage hypothetical generative process when writing a review: (1) deciding on an aspect amongst the set of aspects available for the product or service, and (2) writing the opinion words that are more interrelated to the chosen aspect from the set of all words available in a language. We employ latent Dirichlet allocation to learn the latent aspects distributions for generating the reviews. Experimental results on benchmark datasets show that our proposed method is able to improve the state of the art when the aspects are latent with no surface form in reviews.

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“…Emotion Recognition in Conversation (ERC), also called Conversational Emotion Detection, aims to detect emotional state of speaker based on the signals expressed by speaker (e.g., text, audio, or facial expressions). ERC has potential applications in many fields, such as: (a) Disease Diagnosis [2], assisting doctor to diagnose diseases by medical robot recognizing emotional state of patient when talking with psychologist; (b) Opinion Mining [3], improving the public's trust in government departments or institutions by analyzing online public opinion and measuring the public's experience of policies or services; (c) Conversation Generation [4], injecting recognized emotions into dialogue systems can provide empathetic experiences and significantly improve the usability of dialogue systems and customer satisfaction; (d) Recommender System [5], recognizing user's emotional state from historical chat records between customer service and user, and thus inferring user's potential preference. ERC systems can provide customized services to users and enhance the quality of empathetic interactions with users.…”

Section: Introductionmentioning

confidence: 99%

GA2MIF: Graph and Attention-based Two-stage Multi-source Information Fusion for Conversational Emotion Detection

Li¹,

Wang²,

Lv³

et al. 2022

Preprint

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To enable artificial intelligence in providing empathetic services, multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human multi-sensory integration capabilities. Up until now, there are few studies on multimodal-based conversational emotion recognition. Most of existing Multimodal ERC methods do not model cross-modal interactions and are incapable of extracting inter-modal complementary information. Several graphbased approaches claim to capture inter-modal complementary information, but it is difficult to obtain optimal solution using graph-based models due to the heterogeneity of multimodal data. In this work, we introduce a Graph and Attention-based Twostage Multi-source Information Fusion (GA2MIF) approach for multimodal fusion. Our GA2MIF focuses on contextual modeling and cross-modal modeling leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Crossmodal ATtention networks (MPCATs), respectively. Extensive experiments on two common datasets show that proposed GA2MIF can effectively capture intra-modal local and long-range contextual information as well as inter-modal complementary information, and outperforms existing State-Of-The-Art (SOTA) baselines by an absolute margin.

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Over a decade of social opinion mining: a systematic review

Cited by 63 publications

References 559 publications

Predictive Analysis of COVID-19 Symptoms in Social Networks through Machine Learning

Predictive Analysis of COVID-19 Symptoms in Social Networks through Machine Learning

Latent Aspect Detection from Online Unsolicited Customer Reviews

GA2MIF: Graph and Attention-based Two-stage Multi-source Information Fusion for Conversational Emotion Detection

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