Automated analysis of dream sentiment—The royal road to dream dynamics?

Yu, Calvin Kai-Ching

doi:10.1037/drm0000189

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…SVM achieved the highest accuracy from machine learning models with VADER annotation using feature union which is 0.92 while from deep learning our proposed model CNN-LSTM achieved a significant 0.93 accuracy score. The performance of models with VADER is not significant as compared to TextBlob because TextBlob generates more correlated sentiment in corresponding to text features [56][57][58]. Tabs.…”

Section: Models' Performance Using Vadermentioning

confidence: 99%

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

Lee¹,

Rustam²,

Shahzad³

et al. 2023

Computer Systems Science and Engineering

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With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the 'Drugs.Com' containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.

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Section: Models' Performance Using Vadermentioning

confidence: 99%

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

Lee¹,

Rustam²,

Shahzad³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…Furthermore, many researchers (e.g. Cartwright, 2010; Deliens et al, 2014; Kramer & Barasch, 2000; Levin et al, 2010; Perogamvros & Schwartz, 2012; Walker & van der Helm, 2009; Westermann et al, 2013; Yu, 2015, 2022) have postulated that dreaming serves the function of affect regulation by, for instance, desensitizing adverse subjective feelings, dealing with daytime preoccupations, and decoupling emotions and memories.…”

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confidence: 99%

A moderated mediation study of music engagement, adaptive music listening, and dreaming

Wong

2023

Psychology of Music

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This study aimed to test whether the adaptive functions of music listening would mediate the relation between music engagement and subjective dream intensity and whether this mediation relation would be moderated by the regularity of music listening and level of music training. A total of 236 undergraduate students were invited to complete the Music Engagement Questionnaire, Music Use Questionnaire (MUSE), Adaptive Functions of Music Listening Scale (AFML), and Dream Intensity Scale. The analyses using the PROCESS Marco Models 4 and 75 demonstrated a significant mediation effect of the AFML and significant moderation effects of the MUSE Music Listening and Music Training Indices. This provides the first empirical evidence for the indirect effect of active music engagement on dream intensity via adaptive music listening. In addition, the overall evidence highlights the implication for the significance of music behavior in maintaining mental healthiness. Because an individual’s emotional concerns and difficulty in emotion regulation during waking as reflected by the subconscious process of dreaming are modulated by music behavior and adaptive music listening, directed music engagement for adaptive purposes could form a potential tool for psychological intervention, irrespective of the effect of the regularity of music listening and the background of music training.

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Uncovering lobbying strategies in sustainable finance disclosure regulations using machine learning

Haase,

Sassen

2024

Journal of Environmental Management

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Automated analysis of dream sentiment—The royal road to dream dynamics?

Cited by 5 publications

References 23 publications

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

A moderated mediation study of music engagement, adaptive music listening, and dreaming

Uncovering lobbying strategies in sustainable finance disclosure regulations using machine learning

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