Combining Sentiment Lexicons and Content-Based Features for Depression Detection

Chiong, Raymond; Budhi, Gregorious Satia; Dhakal, Sandeep; Cambria, Erik

doi:10.1109/mis.2021.3093660

Cited by 45 publications

(13 citation statements)

References 21 publications

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“…Machine learning algorithms have been used extensively in various sectors and use cases. The various applications of machine learning algorithms can be observed in healthcare (Waring et al , 2020), energy efficiency and smart cities (Zekić-Sušac et al , 2021), education (Alenezi and Faisal, 2020), social media and mental health (Chiong et al , 2021), fake content detection (Wu et al , 2020) and several other domains. While there are several algorithms used across these sectors, the five most common algorithms, other than the core model, were chosen for building the machine learning pipeline in this paper.…”

Section: Related Workmentioning

confidence: 99%

Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Nanath

Kaitheri

Malik

et al. 2022

JSIT

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Purpose The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news. Design/methodology/approach A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared. Findings The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly. Practical implications Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors. Originality/value While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

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Section: Related Workmentioning

confidence: 99%

Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Nanath

Kaitheri

Malik

et al. 2022

JSIT

View full text Add to dashboard Cite

show abstract

“…This section focuses on the study of several recent works carried out in the sphere of sentiment analysis [ 6 , 11 , 15 , 17 , 22 , 36 , 38 , 48 , 51 , 53 ].…”

Section: Related Workmentioning

confidence: 99%

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

Kaur

Sharma

2023

J Big Data

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There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

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“…Most research investigations on the identification of depression are based on textual data or person-descriptive methods using social media posts to select features. The linguistic elements of the social media content, such as words, Part-of-Speech (POS), Ngrams, and other linguistic properties, are the subject of textual-based featuring [23]. The descriptive-based featured technique emphasizes subject descriptions, including age, gender, employment position, income, drug or alcohol consumption, smoking, and other personal information about the subject or patient [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Depression Detection from Textual Data

et al. 2022

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Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers.

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Combining Sentiment Lexicons and Content-Based Features for Depression Detection

Cited by 45 publications

References 21 publications

Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

Deep Learning for Depression Detection from Textual Data

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