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Proceedings ArticleDOI

Analysis of Machine Learning Algorithms for Predicting Personality: Brief Survey and Experimentation

TLDR
In the current work, real-time Twitter data is fetched through a standard API and thereby created a sample experimental dataset and it was observed that Naive Bayes yielded the highest accuracy of 71.67% as compared to the other classifiers.
Abstract
Use of social media is increasing day by day. A huge amount of textual data, as well as images, continue to explode to the web daily. Personality prediction is an evergreen area of research. Predicting personality with the help of data through social media is a promising approach as this method does not require any questionnaires to be filled by users thus reducing time and increasing credibility. In the current work we have fetched real-time Twitter data through a standard API and thereby created a sample experimental dataset. We have employed several popular machine learning algorithms such as kNN, Naive Bayes and SVM. It was observed that Naive Bayes yielded the highest accuracy of 71.67% as compared to the other classifiers.

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Book ChapterDOI

Investigating the Role of Machine Learning in Detecting Psychological Tension

TL;DR: A detailed review regarding the role and efficiency of popular machine learning algorithms such as Bayes, SVM, ANN, kNN, random forests in determining psychological tension is presented in this article.
Book ChapterDOI

A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation

TL;DR: This work retrieved real-time twitter data pertaining to three currently popular hashtags in the Indian context and carried out extensive experimentation analysis about the prevailing sentiment of a strata of population.
Book ChapterDOI

Investigating the Impact of Data Analysis and Classification on Parametric and Nonparametric Machine Learning Techniques: A Proof of Concept

TL;DR: In this paper, the performance of four popular machine learning classification algorithms (Naive Bayes, decision trees, logistic regression, and random forest) on two popular benchmarked datasets (wine quality dataset and glass identification dataset) is compared.
Book ChapterDOI

HealthCare Data Analytics: A Machine Learning-Based Perspective

TL;DR: In this paper , the design, development, functionalities, and upcoming trends in investigation of Big Data Analytics are discussed along with advantages in relation to infrastructural, organizational, operational, managerial, strategic areas, and articulation of latest trending areas.
Journal ArticleDOI

Personality Detection of Applicants And Employees Using K-mode Algorithm And Ocean Model

TL;DR: In this paper , a model is created to identify applicants' personality types so that employers may find qualified candidates by examining a person's facial expression, speech intonation, and resume.
References
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Journal ArticleDOI

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

TL;DR: The results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.
Journal ArticleDOI

Personality Prediction System from Facebook Users

TL;DR: This study attempts to build a system that can predict a person’s personality based on Facebook user information by implementing some deep learning architectures and succeeds to outperform the accuracy of previous similar research.
Journal ArticleDOI

Detection of suicide-related posts in Twitter data streams

TL;DR: A new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content and the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
Proceedings ArticleDOI

Deep learning based personality recognition from Facebook status updates

TL;DR: This work applies deep learning methods to automatically learn suitable data representation for the personality recognition task using the Facebook status updates data to investigate several neural network architectures on the myPersonality shared task.
Proceedings ArticleDOI

A Multi-Task Cascaded Network for Prediction of Affect, Personality, Mood and Social Context Using EEG Signals

TL;DR: A multi-task cascaded deep neural network which jointly predicts people's affective levels (valence and arousal) and personal factors using EEG signals recorded in response to presentation of affective multimedia content is presented.
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