Predicting Human Personality from Social Media Using a Fuzzy Neural Network

Iatan, Iuliana F.

doi:10.1007/978-3-319-43871-9_3

Cited by 5 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kalish and Robins [26] experimentally investigated the impact of behavior differences between individuals on their immediate network environment, focusing on ego networks consisting of a focal node or ego and the nodes to which the ego is directly connected (the so-called alters) and, if any, the connections between the alters. Their results indicated that the variability [27]- [29], but from a network perspective, the role of links in supporting personality relationships is not yet well recognized.…”

Section: Literature Surveymentioning

confidence: 99%

Survey on personality predication methods using AI

Saloni¹,

Mansi²,

Patil³

et al. 2019

IJAEMS

View full text Add to dashboard Cite

In this paper we present a deep Literature Survey on Personality. Personality is a psychological concept intended to explain the broad range of human behaviors in terms of a few, consistent and observable individual characteristics. In this regard, any technology that includes knowing, predicting and synthesizing human nature is likely to gain from technologies to Personality Computing, i.e. technologies that can deal with human character.This paper is a study of these technologies and seeks to provide not just a strong knowledge and understanding on the state-ofthe art, and also a conceptual model underlying the three main issues discussed in the literature, Electronic Recognition of Character(Inference from behavioral knowledge of an individual or group true character),Automatic recognition of identities(Personality inference other people attribute to an applicant based on observable actions) and Automatic combination of identities (Artificial personality production by means of embodied agents).

show abstract

Section: Literature Surveymentioning

confidence: 99%

Survey on personality predication methods using AI

Saloni¹,

Mansi²,

Patil³

et al. 2019

IJAEMS

View full text Add to dashboard Cite

show abstract

“…Tadesse et al [42] utilize Linguistic Inquiry and Word Count (LIWC) dictionary to extract 85 cognitive-linguistic features. LIWC-based models [14,43] apply the word frequency approach to extract pronouns and identify the psychological and individualized categories. Also, they employ Structured Programming for Linguistic Cue Extraction (SPLICE) to extract 74 linguistic features.…”

Section: Cognitive Perspectivementioning

confidence: 99%

“…Hence, we should devise a model that can infer the pertinence between cognitive features and particular jobs using cognitive insights. To this end, we first extract linguistic features by adopting cognitive textual analytics [14,15] (e.g. LIWC) and subsequently prospect the cognitive features from linguistic features by utilizing the correlation coefficients.…”

Section: Introductionmentioning

confidence: 99%

Cognitive-aware Short-text Understanding for Inferring Professions

Sayna¹,

Hosseini²,

Kangavari³

et al. 2022

Preprint

View full text Add to dashboard Cite

Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents come with excessive lexical noise that makes the inference problem challenging. Secondly, cognitive-semantics are not evident, and important linguistic features are latent in short-text contents. Thirdly, it is hard to measure the correlation between the cognitive short-text semantics and the features pertaining to various occupations. We argue that the multi-aspect cognitive features are needed to correctly associate short-text contents to a particular job and discover suitable people for the careers. To this end, we devise a novel framework that on the one hand, can infer short-text contents and exploit cognitive features, and on the other hand, fuses various adopted novel algorithms, such as curve fitting, support vector, and boosting modules to better predict the occupation of the authors. The final estimation module manufactures the R w-tree via coherence weight to tune the best outcome in the inferring process. We conduct comprehensive experiments on real-life Twitter data. The experimental results show that compared to other rivals, our cognitive multi-aspect model can achieve a higher performance in the career estimation procedure, where it is inevitable to neglect the contextual semantics of users.

show abstract

“…9 "Similarity-based classifiers estimate the class label of a test sample based on the similarities between the test sample and a set of labeled training samples, and the pairwise similarities between the training samples." 10 Image similarity is an important concept in many applications. In Chap.…”

Section: Introductionmentioning

confidence: 99%

“…Our research objective in Chap. 3 is to compare [10] the predictive ability of multiple regression and fuzzy neural model, by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We shall choose to use the fuzzy Gaussian neural network (FGNN) for predicting personality because it handles nonlinearity associated with the data well.…”

Section: Introductionmentioning

confidence: 99%

Issues in the Use of Neural Networks in Information Retrieval

Iatan

2017

Studies in Computational Intelligence

Self Cite

View full text Add to dashboard Cite

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.

show abstract

Predicting Human Personality from Social Media Using a Fuzzy Neural Network

Cited by 5 publications

References 19 publications

Survey on personality predication methods using AI

Survey on personality predication methods using AI

Cognitive-aware Short-text Understanding for Inferring Professions

Issues in the Use of Neural Networks in Information Retrieval

Contact Info

Product

Resources

About