A Dimensional Representation of Depressive Text

Rawat, Tara; Jain, Shikha

doi:10.1007/978-981-15-8335-3_16

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…The authors used mobile technologies to collect input data. Many researchers are working towards detecting loneliness [129], [118], depression [106], [9], [104], [105], or suicidal thoughts [75] However, the results obtained are highly data-dependent and far from cognitive theories of emotions.…”

Section: Semantic / Senticmentioning

confidence: 99%

Emotionally Wrapped Social Media Text: Approaches, Opportunities, and Challenges

Rawat,

Jain

2023

SCPE

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With the evolution of several online platforms for information sharing such as social media, blogs, product review sites, and discussion forums, people have become more proactive in sharing their expectations, views, feelings, and experiences. This large amount of emotionally wrapped data motivates many researchers to perform data mining and present the crux of hidden emotions or mental states in a more presentable and comprehensible manner. It has several applications in different domains such as business, education, psychology, politics, and many more. This paper presents a detailed literature review projected to rigorously analyze the existing approaches to identify the mental or emotional state of a person from unstructured textual data. We include the most relevant papers which were published during 2001-2022. The selected papers are classified into three categories: granularity level, contextual level, and cognition level. Each category is carefully analyzed followed by a detailed and critical discussion. Finally, open challenges, opportunities, applications, and future directives are presented in-depth to facilitate the researchers working in the domain of emotion mining.

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Section: Semantic / Senticmentioning

confidence: 99%

Emotionally Wrapped Social Media Text: Approaches, Opportunities, and Challenges

Rawat,

Jain

2023

SCPE

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TERMS: textual emotion recognition in multidimensional space

et al. 2022

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Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.

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DPre: Effective preprocessing techniques for social media depressive text

Rawat¹,

Jain²

2022

IDT

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Depression has become one of the most common public health issues. Several people with depression rely on social media to express their grief. The text data generated by these users can be exploited to promote study in this area in order to detect early-stage depression and provide support. However, to develop a reliable automatic depression detection system, the social media text cannot be used directly as there is a lot of irrelevant, inaccurate, and noisy information available. Moreover, the basic preprocessing steps which are used with most of the machine learning models have limited functionality and thus lead to lots of information loss. This loss of information is not affordable especially in the domain of affective computing (mental health) for text. In this paper, we present various preprocessing techniques for depressive text, DPre, to obtain readable text from raw and noisy tweets. This method can help in minimizing the loss of information and expressions hidden in the raw tweet. Moreover, the processed and clean text will be ready to input into any machine learning algorithm. The readability of the processed text is evaluated and compared with raw tweets using four readability scores: Flesch Reading Score, Flesch_kincaid Score, the Coleman-Liau Index, and Dale_Chall Score. Compared to basic state-of-art preprocessing methods, the proposed method significantly improved the readability score.

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A Dimensional Representation of Depressive Text

Cited by 4 publications

References 19 publications

Emotionally Wrapped Social Media Text: Approaches, Opportunities, and Challenges

Emotionally Wrapped Social Media Text: Approaches, Opportunities, and Challenges

TERMS: textual emotion recognition in multidimensional space

DPre: Effective preprocessing techniques for social media depressive text

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