redBERT

  1. Chaitanya Pandey

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Abstract

A natural language processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19-related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess the human psyche in times of distress.

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  1. Version published to 10.4018/ijossp.2021070103
  2. ScreenIT

    SciScore for 10.1101/2021.03.02.21252747: (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

    Software and Algorithms
    SentencesResources
    This section breaks down the methods used to achieve this study’s main contributions, proposing a topic model based on unsupervised learning with a collaborative deep-learning model that draws on BERT to analyse COVID-19 related comments in various subreddits.
    BERT
    suggested: (BERT, RRID:SCR_018008)
    BERT-LARGE is trained mainly on raw text data from Wikipedia (3.5B words) and a free book corpus (0.8B words) [2].
    Wikipedia
    suggested: (Wikipedia, RRID:SCR_004897)
    BIOBERT [21] and SCIBERT [22] are trained using the same unsupervised training techniques as the main models (MLM/NSP/SOP).
    BIOBERT
    suggested: (BioBERT, RRID:SCR_017547)

    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.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    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.

  3. Version published to 10.1101/2021.03.02.21252747v1 on medRxiv