Social Emotional Opinion Decision with Newly Coined Words and Emoticon Polarity of Social Networks Services

Yang, Jin Sol; Ko, Myunggon; Chung, Kwang Sik

doi:10.3390/fi11080165

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…In order to deeply analyze the social status and its changes of different female groups, this survey also investigated five typical groups, namely, children, the elderly, college students, people affected by migration and high-level talents. Most studies are based on the data of SSCW3 due to its authority and reliability, but SSCW3 ignores IPV data of a marginalized group, namely LGBT [ 50 ]. The research methods of questionnaires and interviews have regional limitations since the data can only be collected from a specific region.…”

Section: Literature Reviewmentioning

confidence: 99%

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Zeng

Tai

et al. 2022

Healthcare

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The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour.

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Section: Literature Reviewmentioning

confidence: 99%

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Zeng

Tai

et al. 2022

Healthcare

View full text Add to dashboard Cite

show abstract

“…What was also learned is that an algorithm needs additional information for classifying sarcasm correctly (Poria et al, 2016). Thus, analysis of a tweet for its genuine emotion was extremely difficult (Asghar et al, 2018;Yang et al, 2019). Consequently, emoticons were removed entirely from all the tweets during the pre-processing phase.…”

Section: Covid'19 Twitter Dataset and Analysismentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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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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“…The statistical approaches are based on the use of machine learning methods, such as the support vector machines (SVMs), Bayesian networks, or deep learning [55][56][57]. In addition, by combining the advantages of both approaches, hybrid techniques have been used in many studies to try to improve the results, such as in [58,59].…”

Section: Affective Computing and Sentiment Analysismentioning

confidence: 99%

Ordered Weighted Averaging for Emotion-Driven Polarity Detection

2021

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The overall rating of an opinion can generally be considered as the aggregation of the individual ratings of all features of that opinion. Nevertheless, there are cases in which the overall rating differs substantially from the mean or weighted mean of the ratings of the individual features. These cases can be explained in terms of user mood. To address this problem, this study introduces a fuzzy framework for computing user mood based on SenticNet and sentic patterns, which are used to guide an ordered weighted averaging operator. This operator allows the aggregation to be computed in such a way as to provide an understanding of why some positive or negative aspects are considered to a greater or lesser extent. The performance and advantages of this proposal are illustrated in depth via a variety of scenarios applied to real data. The results show a promising framework applicable to other tools, such as customized recommender systems or decision support systems.

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Social Emotional Opinion Decision with Newly Coined Words and Emoticon Polarity of Social Networks Services

Cited by 13 publications

References 12 publications

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

Ordered Weighted Averaging for Emotion-Driven Polarity Detection

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