Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation
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Abstract
Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection. We present an evaluation of the framework and a pilot system. Results of confirm the effectiveness of the proposed framework for topic trends and emotion detection of COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics (feelings), where negatives are “Anger” (8.5% of tweets), followed by “Fear” (5.2%), “Anticipation” (53.6%) and positive emotional semantics are “Joy” (14.7%) and “Trust” (11.7%). Semantic trends of safety issues related to staying at home rapidly decreased within the 28 days and also negative feelings related to friends dying and quarantined life increased in some days. These findings have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. The framework presented here has potential to assist in such monitoring by using as an online emotion detection tool kit.
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SciScore for 10.1101/2021.01.16.21249943: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
This research has some limitations, e.g the size of dataset, data inclusion limited to emotions based on texts of COVID-19 issues. Currently, our data consists of 1,047,968 tweets based on #StatAtHome tweets from 28-April to 2-June of 2020. Although more tweets can be extracted based on #StayAtHome, we believe that the number of current …
SciScore for 10.1101/2021.01.16.21249943: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
This research has some limitations, e.g the size of dataset, data inclusion limited to emotions based on texts of COVID-19 issues. Currently, our data consists of 1,047,968 tweets based on #StatAtHome tweets from 28-April to 2-June of 2020. Although more tweets can be extracted based on #StayAtHome, we believe that the number of current tweets is sufficient to draw reasonable conclusions to direct possibilities of uncovering importance of consequences of public health orders and restrictions. We acknowledge that the imbalanced dataset representing different emotions is also a limitation of our work. We did not consider slang or emoticons to compute emotions in the tweet contents, it would be useful to build a new emotional lexical to cover slang words related to COVID-19 issues. A significantly longer temporal horizon longitudinal dataset would allow us using LSTM on sequences of tweets, as well as replacing the cross-validation. train/test approach with one based time-stamp rather than the cross-validation approach. Further advantage of a temporally larger dataset is an opportunity of a longitudinal study combining geographically-based tweeter -detected emotions with COVID-19 incidences and expanded public health regulations to enable geographic-area targeted public health decision making. The framework we developed showed potential to accurately uncover emotional responses and temporal trend detection of mood changes due to quarantine related public health orders.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from scite Reference Check: We found no unreliable references.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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SciScore for 10.1101/2021.01.16.21249943: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore …
SciScore for 10.1101/2021.01.16.21249943: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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