Personality Prediction of Social Network Users Using Ensemble and XGBoost
01 Jan 2020-pp 133-140
TL;DR: Predicting personality using social media is a new approach where direct interaction with people can be eliminated and accurate predictions can be built and high accuracy of 82.59% with an Ensemble is indicated.
Abstract: Machine learning has gained tremendous attention from researchers recently. It has wide applications in tasks such as prediction and classification. Current work focuses on the effective detection of the personality of social network users. Personality is a combination of one’s thinking and behavior. Having knowledge about personality of a person has many applications in real world such as varied recommendation systems or HR departments. Personality of a person can be better understood by interacting with him/her. Predicting personality using social media is a new approach where direct interaction with people can be eliminated and accurate predictions can be built. Although different machine learning methods have been used by researchers recently for the task of prediction, the use of Ensembles has not been explored. Current work focuses on advanced classifiers such as XGBoost and Ensemble for prediction. Experimentation on the real-time Twitter dataset indicates high accuracy of 82.59% with an Ensemble. These results are encouraging for future research.
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TL;DR: The proposed method was validated on the Essay and YouTube datasets by conducting a series of experiments and the empirical results demonstrated the superiority of the proposed method on both datasets compared to both machine learning and deep learning methods for the task of personality recognition.
3 citations
01 Jan 2021
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.
Abstract: Psychological tension is a growing concern worldwide and has gradually victimized individuals across differing age groups, gender and nationality globally. Despite the omnipresence of technology, especially in the field of healthcare, psychological tension continues to be a powerful and widespread disorder, implications of which manifest in the form of varied physical and mental ailments. This paper presents a detailed review regarding the role and efficiency of popularly used machine learning algorithms such as Bayes, SVM, ANN, kNN, random forests in determining psychological tension. Systematic analysis of the physiological features, their thresholds and the scenario in question leads to successful classification of tension as low, medium or high. Knowledge-based systems that could effectively diagnose psychological tension with scientific quantification techniques shall be immensely useful in studying human affect and also successfully mitigate tension/strain by promoting its early automated/semi-automated detection, thus largely contributing to mankind.
2 citations
01 Jan 2021
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.
Abstract: Twitter analytics is a classic research area especially with the widespread presence of Big Data in various online media such as—social network sites, online portals for shopping, e-commerce, forums, chats, recommendation systems, and online services. Ascertaining the sentiment behind, the various types of tweets by different persons can provide great insights on various aspects including behavioral patterns. Besides highlighting the newest trends in the field, we 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. Inclusion of current challenges, future trends and applications of sentiment analysis from Twitter data makes this novel work very useful for fellow researchers.
2 citations
TL;DR: A hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN- classifiers with DNN-classifier in a proposed two-stage decision fusion scheme.
Abstract: In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.
2 citations
TL;DR: Wang et al. as discussed by the authors proposed KGrAt-Net, which is a knowledge graph attention network text classifier for automatic personality prediction according to the Big Five personality traits, and applied the attention mechanism to the most relevant parts of the graph to predict the personality traits of the input text.
Abstract: Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2,467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.
1 citations
References
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270 citations
TL;DR: A comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube is performed.
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173 citations
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.
113 citations
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.
Abstract: Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present 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. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
107 citations
TL;DR: The impact of the Big Five personality traits on human online information seeking is explored and individuals high in conscientiousness performed fastest in most information-seeking tasks, followed by those high in agreeableness and extraversion.
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94 citations