ArticleJMIR formative research2020
Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count.
Article in JMIR formative research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mode of social contact is associated with momentary verbal communication of emotion and well-being in older adults.Communications psychology · 2025Article
- Identifying Factors Associated With Heightened Anxiety During Breast Cancer Diagnosis Through the Analysis of Social Media Data on Reddit: Mixed Methods Study.JMIR cancer · 2024Article
- Childhood conduct problems and parent-child talk during social and nonsocial play contexts: a naturalistic home-based experiment.Scientific reports · 2024Article
- Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter.Frontiers in public health · 2024Article
- Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure.Frontiers in psychology · 2024Article
- Talking Cancer-Cancer Talking: A Linguistic and Thematic Analysis of Patient Narratives.Journal of patient experience · 2024Article
- Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.Journal of medical Internet research · 2023Article
- Public health messages during a global emergency through an online community: a discourse and sentiment analysis.Frontiers in digital health · 2023Article
- How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media.International journal of environmental research and public health · 2022Article
- Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment.Frontiers in psychology · 2021Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGiven the high volume of text-based communication such as email, Facebook, Twitter, and additional web-based and mobile apps, there are unique opportunities to use text to better understand underlying psychological constructs such as emotion. Emotion recognition in text is critical to commercial enterprises (eg, understanding the valence of customer reviews) and to current and emerging clinical applications (eg, as markers of clinical progress and risk of suicide), and the Linguistic Inquiry and Word Count (LIWC) is a commonly used program.
objectiveGiven the wide use of this program, the purpose of this study is to update previous validation results with two newer versions of LIWC.
methodsTests of proportions were conducted using the total number of emotion words identified by human coders for each emotional category as the reference group. In addition to tests of proportions, we calculated F scores to evaluate the accuracy of LIWC 2001, LIWC 2007, and LIWC 2015.
resultsResults indicate that LIWC 2001, LIWC 2007, and LIWC 2015 each demonstrate good sensitivity for identifying emotional expression, whereas LIWC 2007 and LIWC 2015 were significantly more sensitive than LIWC 2001 for identifying emotional expression and positive emotion; however, more recent versions of LIWC were also significantly more likely to overidentify emotional content than LIWC 2001. LIWC 2001 demonstrated significantly better precision (F score) for identifying overall emotion, negative emotion, and anxiety compared with LIWC 2007 and LIWC 2015.
conclusionsTaken together, these results suggest that LIWC 2001 most accurately reflects the emotional identification of human coders.
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