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Skip search results from other journals and go to results- 2 JMIR Cancer
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After exploring the Redditor characteristics of our dataset, we used LIWC-22 [28] to understand the key psychological domains within the Hi B-RC.
LIWC-22 is a text analysis application that maps psychosocial constructs to words, phrases, and linguistic constructions [28]. Linguistic Inquiry and Word Count (LIWC) processes text using software and a dictionary, where the dictionary contains groups of words that relate to a particular domain (eg, positive or negative tone).
JMIR Infodemiology 2025;5:e65632
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Aside from content analysis, such as Psychodynamic Work, Object Rating System [16], many studies adopted computerized textual analysis systems such as dt Search [17], Linguistic Inquiry, and Word Count (LIWC) to track levels of cohesion through text [18-21].
JMIR Cancer 2024;10:e43070
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The language analysis tool Linguistic Inquiry and Word Count (LIWC; version 2022) [29] was used to analyze the text data. The LIWC is designed to measure psychometric properties in language. As noted by Boyd [30], LIWC analysis typically works best with texts exceeding 50 words (shorter texts with a minimum of 10 words may still yield some insights, but the results may be less accurate). This is because LIWC dictionaries work by calculating the relative percentage of a word’s occurrence in a body of text.
J Particip Med 2024;16:e49262
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Linguistic variables were determined using Linguistic Inquiry and Word Count (LIWC; see below for details).
The sample examined in this study was based on the previous evaluation of krisenchat, in which the sample consisted of those who completed a subsequent feedback survey after the counseling session [20]. Thus, out of a total of 11,031 users in the above-mentioned time period, 6962 (63.1%) completed a feedback survey. The chat messages of these 6962 users were analyzed.
JMIR Form Res 2024;8:e51795
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One of the most commonly used natural language tools in text analysis is the Linguistic Inquiry and Word Count (LIWC) [22]. The most up-to-date version, LIWC-22, has an internal dictionary of over 12,000 words categorized into various groups intended to assess different psychosocial constructs [22]. Numerous categories related to first-person pronoun use and negatively valenced emotion and tone words have been shown to be associated with depression symptom severity [23-25].
JMIR Ment Health 2023;10:e48517
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The Linguistic Inquiry and Word Count (LIWC) software [27] was applied to all textual data to obtain a reference score for positive and negative emotions. LIWC scanned each line of the conversation for positive and negative emotions. We hypothesized that correlations between LIWC and AICF outputs would be strong (≥0.7).
To validate AICF by the close to real-time participant emotional states, we grouped the 9 emojis into positive and negative states, with the neutral emoji excluded.
JMIR Cancer 2023;9:e40113
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Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights
The data were analyzed using 2 different natural language processing approaches: (1) the Linguistic Inquiry and Word Count (LIWC) computational tool [59] and (2) the Google Perspective API [60].
LIWC is a natural language processing tool that measures psychological processes in texts by counting the percentage of words in a given tweet that fall into prespecified categories.
JMIR Infodemiology 2022;2(2):e37635
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This study has been enabled by the development of computational linguistic tools, of which the most widely used is the Linguistic Inquiry and Word Count (LIWC) [38,39]. In addition to categorizing and counting words, LIWC offers an overview of the statistical distribution of words within predefined and psychologically meaningful categories.
JMIR Form Res 2022;6(6):e33036
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In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study
In fact, Capecelatro et al [31] found depression to be unrelated to all Linguistic Inquiry and Word Count (LIWC) emotion categories.
Initially, studies concerning typing dynamics used external computer keyboards to predict stress and depression, among other emotions [61-65]. More recent studies have tried to use soft keyboards on smartphones for emotion, depression, and bipolar disorder detection [66-69].
JMIR Ment Health 2022;9(2):e31724
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To assess the frequency of these indicators, we used the Linguistic Inquiry and Word Count (LIWC) text analysis program [40]. LIWC is based on a word count algorithm that searches each text unit for words that are assigned to prespecified language categories in its internal dictionary. Words in a given text are matched to these categories and counted to determine the frequency of each category in the text.
J Med Internet Res 2021;23(7):e28346
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