A quantitative text analysis approach to describing posttrauma nightmares in a treatment-seeking population.

Paquet, Caitlin B.; Cogan, Chelsea M.; Davis, Joanne L.

doi:10.1037/drm0000128

Cited by 10 publications

(14 citation statements)

References 35 publications

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“…Most aggressors in nightmares are, incidentally, male perpetrators (Schredl & Göritz, 2018). Post-trauma nightmares, compared with typical dreams, have reduced leisure word counts and increased risk words, as well as higher rates of singular first-person pronouns (Paquet et al, 2020). Notably, "finding money" and "eating delicious foods" are both positive typical dream themes (Nielsen et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

The relationship between dreams and subsequent morning mood using self-reports and text analysis

Mallett¹,

Picard-Deland²,

Pigeon³

et al. 2021

Preprint

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While material from waking life is often represented in dreams, it is less clear whether and how dreams impact waking life in return. Here, we assessed whether dream mood and content from home diaries predict subsequent waking mood using both subjective self-report and an objective automated word detection approach. Subjective ratings of dream and morning mood were highly correlated within participants for both negative and positive valence, suggesting that dream mood persists into waking. Text analyses revealed similar relationships between affect words in dreams and morning mood. Moreover, dreams referencing death or the body were related to worse morning mood, as was first-person singular pronoun usage (e.g., “I”). Dreams referencing leisure or ingestion, or including first-person plural pronouns (e.g., “we”), were related to better morning mood. Together, these results suggest that subjective experiences during sleep, while often overlooked, may be an important contributor to the emotion processing functions of sleep.

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Section: Discussionmentioning

confidence: 99%

The relationship between dreams and subsequent morning mood using self-reports and text analysis

Mallett¹,

Picard-Deland²,

Pigeon³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[17] Ref. [13] classification effect for recessive expressions is poor. e proposed model integrates CNN, BiGRU, and attention mechanism and extracts the semantic features of the text from bottom to top through "word-sentence-text."…”

Section: Proposed Modelmentioning

confidence: 98%

“…Text sentiment analysis refers to the use of computers to automatically calculate and process human natural language texts, involving comprehensive problems in mathematics, statistics, and computational science. Its goal is to solve the problem of computer and text sentiment analysis in order to maximize the extraction of valuable information and achieve better human-computer interaction [13].…”

Section: Related Researchmentioning

confidence: 99%

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Sentiment Analysis of Student Texts Using the CNN-BiGRU-AT Model

Yan

Zhou

Qian

et al. 2021

Scientific Programming

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For most current sentiment analysis models, it is difficult to capture the complex semantic and grammatical information in the text, and they are not fully applicable to the analysis of student sentiments. A novel student text sentiment analysis model using the convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism, called CNN-BiGRU-AT model, is proposed. Firstly, the text is divided into multiple sentences, and the convolutional neural network (CNN) is used to extract n-gram information of different granularities from each sentence to construct a sentence-level feature representation. Then, the sentences are sequentially integrated through the bidirectional gated recurrent unit (BiGRU) to extract the contextual semantic information features of the text. Finally, an attention mechanism is added to the CNN-BiGRU model, and different learning weights are applied to the model by calculating the attention score. The top-down text features of “word-sentence-text” are input into the softmax classifier to realize sentiment classification. Based on the weibo_senti_100 k dataset, the proposed model is experimentally demonstrated. The results show that the accuracy rate and recall rate of its classification mostly exceed 0.9, and the F1 value is not lower than 0.8, which are better than the results of other models. The proposed model can provide a certain reference for the related students’ text sentiment analysis research.

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“…Recently, LIWC has been used to describe and analyze post‐trauma nightmares—or PTNM (Paquet et al, 2020a, 2020b). Paquet et al (2020a) found that the PTNM of a treatment‐seeking sample, relative to dreams in a nonclinical sample, contain more references to negative emotions and perceptual experiences, as well as are considered to have less of an analytic nature. Additionally, there were no differences detected between PTNM and dreams in the use of cognitive language.…”

Section: Introductionmentioning

confidence: 99%

Implications of cognitive‐oriented language within posttrauma nightmare narratives

Meador

Paquet

Cogan

et al. 2022

Applied Cognitive Psychology

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While rescription-based therapies are effective treatments for chronic post-trauma nightmares, mechanisms of change have not yet been defined. We examined the relationship between the use of cognitive-related words in nightmare narratives and the posttraumatic cognitions inventory (PTCI) in a sample of 25 trauma-exposed individuals undergoing rescription therapy for post-trauma nightmares. In addition to exploring the relationship between different types of cognitive language use and PTCI scores, the relationship between changes in their frequency of use and posttrauma cognitions were also analyzed. We found that cognitive language use as a whole in addition to the subcategories of tentative, discrepancy, and insight words within the nightmare are significantly negatively related to baseline PTCI scores, while analytic language use was positively correlated. However, cognitive language use in the rescription did not relate to PTCI scores post-treatment. Furthermore, changes in the of use cognitive language, between the nightmare and the rescription, are significantly negatively associated with severity of posttraumatic cognitions at the post-assessment. These results demonstrate the importance of understanding cognitive language in nightmare imagery as it may relay important information about treatment outcomes.

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A quantitative text analysis approach to describing posttrauma nightmares in a treatment-seeking population.

Cited by 10 publications

References 35 publications

The relationship between dreams and subsequent morning mood using self-reports and text analysis

The relationship between dreams and subsequent morning mood using self-reports and text analysis

Sentiment Analysis of Student Texts Using the CNN-BiGRU-AT Model

Implications of cognitive‐oriented language within posttrauma nightmare narratives

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