The General Inquirer: A Computer Approach to Content Analysis.

Hartman, John J.; Stone, Philip J.; Dunphy, Dexter; Smith, Marshall S.; Ogilvia, Daniel M.

doi:10.2307/2092070

Cited by 41 publications

(35 citation statements)

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“…[10] demonstrates popular hand-composed lexicons used for sentiment analysis. This predominantly includes six lexicons, namely: Vader [11], AFINN [12], Emotion Lexicon [13], Bing Liu's Lexicon [14], MPQA Lexicon [15], and General Inquirer [16]. However, compiling such a lexicon is labor intensive and might only cover some of the words from the corpus that represent or indicate some sentiment.…”

Section: Category Based Embedding (Cbe)mentioning

confidence: 99%

A Survey of Text Representation and Embedding Techniques in NLP

et al. 2023

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Natural Language Processing (NLP) is a research field where a language in consideration is processed to understand its syntactic, semantic, and sentimental aspects. The advancement in the NLP area has helped solve problems in the domains such as Neural Machine Translation, Name Entity Recognition, Sentiment Analysis, and Chatbots, to name a few. The topic of NLP broadly consists of two main parts: the representation of the input text (raw data) into numerical format (vectors or matrix) and the design of models for processing the numerical data. This paper focuses on the former part and surveys how the NLP field has evolved from rule-based, statistical to more context-sensitive learned representations. For each embedding type, we list their representation, issues they addressed, limitations, and applications. This survey covers the history of text representations from the 1970s and onwards, from regular expressions to the latest vector representations used to encode the raw text data. It demonstrates how the NLP field progressed from where it could comprehend just bits and pieces to all the significant aspects of the text over time.

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Section: Category Based Embedding (Cbe)mentioning

confidence: 99%

A Survey of Text Representation and Embedding Techniques in NLP

et al. 2023

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“…The output score indicates the extent to which the text contains the construct of interest (e.g., the number of positive emotion words). Examples of the closed-vocabulary approach are the General Inquirer (Stone et al, 1966), the Medical Research Council psycholinguistic database (MRC; Wilson, 1988), the Honesty-Humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness to Experience (HEXACO) text-to-personality technique (HTTP; Holtrop et al, 2022), but by far the most popular approach is LIWC, which, at the time of writing this article, counts a little more than 10,000 citations in Google Scholar. Since the goal of the present meta-analysis is to identify the linguistic markers of personality, and since a large number of LIWC studies has already been accumulated, we opted to meta-analyze the results of LIWC research.…”

Section: Text-based Personality Assessment and The Liwcmentioning

confidence: 99%

“…One explanation for the relation between self-reported personality traits and linguistic categories is that word frequency reflects what individuals are paying attention to (Baldwin, 1942;Boyd & Schwartz, 2021;Stone et al, 1966). That is, individuals thinking, for example, about "death, sex, money, or friends will refer to them in their writing or conversation" (Tausczik and Pennebaker, 2010, p. 30; see same reference for a summary of empirical findings on "words as attention").…”

Section: Liwc and Personality Traitsmentioning

confidence: 99%

The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and the Linguistic Inquiry and Word Count (LIWC).

Koutsoumpis¹,

Oostrom²,

Holtrop³

et al. 2022

Psychological Bulletin

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The Linguistic Inquiry and Word Count (LIWC) is a popular closed-vocabulary text analysis software program that is used to understand whether individuals' use of linguistic categories (i.e., word categories, such as negative affect) depends on their personality traits. Here, we present the first meta-analysis of the relations between the Big Five personality traits and 52 linguistic categories of the English language. Across 31 eligible samples (n = 85,724), the results showed that (a) self-reported personality traits are significantly correlated with linguistic categories, but the effect sizes are relatively small (the strongest effect sizes between the Big Five and linguistic categories ranged from |ρ| = .08 to .14, and the 52 LIWC categories explained on average 5.1% of personality variance); (b) observerreported personality traits are significantly correlated with linguistic categories, with the effect sizes being small-to-medium (|ρ| = .18-.39, explaining 38.5% of personality variance); (c) 20 linguistic categories (out of 260; 5 Personality Traits × 52 LIWC Categories) correlated both with self-and observer-reported personality traits (the "kernel of truth" in linguistic markers of personality); and (d) 10 study, sample, and task characteristics significantly moderated the correlations of the linguistic categories with personality traits, showing that the effect sizes were mainly stronger for longer texts and older LIWC versions, among others. Public Significance StatementThis meta-analysis identifies the linguistic categories (i.e., word categories, such as negative affect) that individuals use depending on their personality traits, as well as the linguistic categories that other people use to draw personality inferences. Individuals indeed use specific linguistic categories depending on their personality traits and others use specific linguistic categories to draw personality inferences, but those relations are dependent on study and tasks characteristics (e.g., text length, Linguistic Inquiry and Word Count version).

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“…The other is a computer‐based approach that uses multivariate analysis to automatically identify and classify groups of words that frequently appear in the same document or groups of documents that contain many common words 13,14 . The latter method of computer‐based text mining has been employed since its use was first reported in 1966, 15 with the rapid developments in computer performance. In particular, this method of large data analysis has been developed and widely used in the commercial field for various purposes, such as understanding customer needs 16 .…”

Section: Introductionmentioning

confidence: 99%

Quantitative text analysis of the mechanisms of tooth injury: Analysis of accidents in five sports that occurred in 15 years under school control

Shimizu

Tanabe

Hayashi

et al. 2022

Dental Traumatology

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Background/Aim: Dental trauma may occur in school settings and it can impact patients' quality of life. Analysis of the occurrences of dental trauma is essential in identifying preventive methods. There are numerous reports of such accidents but most are quantitative analyses with limited data. The aim of this study was to extract information on the mechanisms of injuries that leads to prevention by performing a qualitative analysis of the occurrence of dental trauma.

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The General Inquirer: A Computer Approach to Content Analysis.

Cited by 41 publications

References 0 publications

A Survey of Text Representation and Embedding Techniques in NLP

A Survey of Text Representation and Embedding Techniques in NLP

The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and the Linguistic Inquiry and Word Count (LIWC).

Quantitative text analysis of the mechanisms of tooth injury: Analysis of accidents in five sports that occurred in 15 years under school control

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