Detection of Early Depression Signals Using Social Media Sentiment Analysis on Big Data

Nair, Sanjeev; Ashok, Amritha; Pai, Reshma; Narayanan, A. G. Hari

doi:10.1007/978-981-16-3728-5_31

Cited by 3 publications

(6 citation statements)

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“…Big data is using enormous amounts of data to gain current, insightful insights into a specific population. This involves examining the comments made by thousands of users on various social media platforms to get a sense of the attitude of your online audience as a whole 3,58 . Big data uses complex algorithms and artificial intelligence models to determine a person's happiness by observing their online behavior.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…This involves examining the comments made by thousands of users on various social media platforms to get a sense of the attitude of your online audience as a whole. 3,58 Big data uses complex algorithms and artificial intelligence models to determine a person's happiness by observing their online behavior. One of the most crucial resources for businesses looking to succeed online over the long term is big data on social media.…”

Section: Affordable Costsmentioning

confidence: 99%

“…Obtaining accurate information from various sources is essential, but it's equally important to prevent the spread of false information. 31,58 Analytical findings that are inaccurate or unreliable may result from poor-quality, outdated, or incorrect data. Although machine learning and data mining can help us lessen the effects of inaccurate data, they cannot ensure its complete accuracy.…”

Section: Term Definitionmentioning

confidence: 99%

“…Nair et al 58 predicted using supervised ML based on Naive Bayes, decision trees, SVM, and k‐nearest neighbors on large amounts of data to determine the accuracy of the prediction. Social media has changed how many people experience reality as people have started to relate their lives to these technological devices.…”

Section: Organization Of Big Data In Social Mediamentioning

confidence: 99%

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A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

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SummaryExamining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high‐quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data‐trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1‐score, and 5% run time complexity.

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Section: Discussion and Comparisonmentioning

confidence: 99%

Section: Affordable Costsmentioning

confidence: 99%

Section: Term Definitionmentioning

confidence: 99%

Section: Organization Of Big Data In Social Mediamentioning

confidence: 99%

See 2 more Smart Citations

A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

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“…To analyze longer text sequences, the proposed model employs biLSTM. Nair et al (2022) employed machine learning techniques to predict early signs of depression. Classifiers such as naive Bayes, decision trees, SVM, and k-nearest neighbors are employed on big data extracted from social media.…”

Section: Related Workmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.

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