Personality Prediction of Social Network Users Using Ensemble and XGBoost

Kunte, Aditi V.; Panicker, Suja

doi:10.1007/978-981-15-2414-1_14

Cited by 21 publications

(13 citation statements)

References 16 publications

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“…More in detail, XGB is a recent algorithm resulted in being highly efficient in regression/classification competition (see www.kaggle.com) and many real-world applications (Ivanov et al, 2020;Kunte & Panicker, 2020; Figure 1. Illustration of a typical scenario occurring during the procedure of hyperparameter tuning for every machine learning method.…”

Section: Ensemble Methodsmentioning

confidence: 99%

“…More in detail, XGB is a recent algorithm resulted in being highly efficient in regression/classification competition (see http://www.kaggle.com) and many real‐world applications (Ivanov et al., 2020; Kunte & Panicker, 2020; Luckner et al., 2017). XGB employs second‐order derivative during gradient descent resulting in faster and more refined performance.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

et al. 2021

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The near-Earth environment is composed of different plasma populations that cover an energy range of several orders of magnitude. The plasmasphere is the coldest one (∼1 eV) and typically extends from the top of the ionosphere up to 4-6 Earth radii (R E ). Its shape, composition and extension change in response to geomagnetic activity variation and, due to its preponderant contribution to the mass density, plays a crucial role in exciting plasma-waves and driving their interaction with more energetic particles (Liemohn, 2006;Thorne, 2010). During major storms the outer boundary, the plasmapause, also defined as the plasmasphere boundary layer (PBL, Carpenter & Lemaire, 2004), can move earthward as near as 1.5-2 R E , and the recovery to the pre-storm conditions can last for several days.For its central role in the space weather context, a real-time monitoring of the cold plasma in the inner magnetosphere would be an essential task to achieve. A prototype of such system was developed inside the PLASMON FP7 European project (Lichtenberger et al., 2013) during which the European quasi-Meridional Magnetometer Array (EMMA) was established. EMMA provides a unique opportunity to monitor the plasmasphere dynamics in near real-time. It consists of 27 ground-based magnetic stations approximately located along the same magnetic meridian and mapping into magnetic L-shells in the range 1.6-6.1, where L is the McIlwain parameter evaluated using the IGRF. Using Ultra Low Frequency (ULF) signals simultaneously observed by pairs of stations it is possible to infer the radial distribution of the equatorial plasma mass density in the longitudinal sector identified by the network (Del Corpo et al., 2019;Lichtenberger et al., 2013).The core of this monitoring system is the gradient method proposed by Baransky et al. (1985) and further developed by Waters et al. (1991): By performing Fourier cross-spectral analysis of the magnetic signal recorded by two stations slightly separated in latitude, it is possible to estimate the resonance frequency of the field line whose footprint is halfway between the stations. Around the field line resonance (FLR) frequency

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Section: Ensemble Methodsmentioning

confidence: 99%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

et al. 2021

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“…Then, the results have fed into an ensemble learning model that combines multiple classifiers' outputs, to acquire more reliable prediction. Having the same objectives, other researchers ( [47][48][49]) have questioned the usefulness of such an approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Ramezani,

Feizi-Derakhshi,

Balafar

2022

Preprint

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How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short * m

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“…Several different machine learning methods have been used by researchers for prediction. For example, one study employed advanced classifiers such as XGBoost and ensemble for prediction, finding that ensemble has high accuracy (82.59%) for real-time Twitter datasets [19]. Significant improvements can be made by achieving a 1.0 ROC AUC score with SGD and super learner in research for personality recognition on Twitter in the Indonesian language [4].…”

Section: Related Studiesmentioning

confidence: 99%

“…Machine learning is a growing branch of artificial intelligence that learns from data patterns to make decisions without human intervention. Machines can be trained to cognize and assess individuals' personalities [19]. Similarly, by employing support vector machine and linear regression with an LFM-1b dataset, user demographics might be identified based on music listening information [14].…”

Section: Introductionmentioning

confidence: 99%

Supervised learning and resampling techniques on DISC personality classification using Twitter information in Bahasa Indonesia

Utami¹,

Oyong²,

Raharjo

et al. 2021

ACI

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PurposeGathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).Design/methodology/approachIn this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.FindingsThe authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).Originality/valueThe process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.

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Personality Prediction of Social Network Users Using Ensemble and XGBoost

Cited by 21 publications

References 16 publications

Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Supervised learning and resampling techniques on DISC personality classification using Twitter information in Bahasa Indonesia

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