Survey on personality predication methods using AI

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-5, Issue-12, Dec-2019]
https://dx.doi.org/10.22161/ijaems.512.7 ISSN: 2454-1311
www.ijaems.com Page | 655
Survey on personality predication methods using
AI
Jadhav Saloni, Jaras Mansi, Patil Payal, Sonawane Sunita.
Shatabdi Institute of Engineering & Research, Department of Computer, Pune University, Nashik, India
Abstract— 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-of – the 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).
Keywords—Personality prediction, AI.
I. INTRODUCTION
The growth of social networks is the result of social change, t
he sharing of information, interaction and collaboration betw
een changes. On the other hand, the role of social networks in
everyday life is that, and the restoration of the network of
real social relationships has affected even to a large extent.
At the same time, due to the easy recording, acquisition and
analysis of the behavior and status of social media networks,
social computing has become an important research content
in the field of computer science and computers.Because the
behavior and personality of people are closely linked, there is
a wide intellectual value and business prospects for making
personality prediction. The results show that commercial
personality and commodity selection are significantly
correlated.Personality data can be used extensively in
personalized recommendations for ads and merchandise [1],
[2].Personality is a high degree of generalization of different
characteristics of mankind, even in the same situation,
different people will show different characteristics that come
from each person's different personality.Personality
psychology is one of the branches of psychology, mainly
through the outward actions of individuals to discern and
analyze the relation between the intrinsic characteristics of
individuals [4].Normally, psychology uses personality traits
to describe the personality of individuals, describing the
actions and interests of the user [5].MBTI (Myers Briggs
Type Indicator) and the big five personality model (openness,
acceptability, conscientiousness, extraversion, and
neuroticism) are widely used personality models. The
behavior of the user's social network is closely related to his
character traits. Hamburger et al. have been studying the
relationship between user behavior and personality as early
as 2000. Their research suggests that a correlation exists
between both the personality of the user and the social
services that they use. Between them, the outgoing
personality is more inclined towards entertainment; the
neurotic personality is less use of information services, and
more popular social services [6]. [7] Used the usage habits of
the keyboard and mouse to predict personality traits. Because
social network user data can represent to a large extent their
true personality traits, it is natural to use the social data of the
user to predict personality. [9] Used the social data analysis
of Facebook to assess that the extraversion and attention
character had a huge positive relationship with the user's use
of social network comfort. [ 10 ] By designing social
networking apps on Facebook, encouraging users to
participate in the big five personality test, using customer
information as a data set to analyze, analysis found that the
network size of the user does not reflect the social status and
value of the user. To predict the relationship between user
behavior and their personality traits, researchers use different
research methods. Bai and others have recently released the

state update, using a variety of machine learning algorithms
such as Naive Bayesian (NB), support vector machine
(SVM) and decision tree, to predict the personality of 335
users online. The results indicate that the C4.5 decision tree
algorithm can have the greatest predictive effect and verify
that the machine learning algorithm can effectively predict
the personality of social media network users [11].
Verhoeven B et al. used an integrated method to determine
the personality of the user to resolve the issue of incomplete
training data and integrated the data of various organizational
forms with the integrated method and proved the validity by
experiment [12]. Through the current academic results, we
may find that there is some correlation between the social
media network and the personality of the user, but the use of
social networks to predict the personality of the survey
results of the user is relatively small. On this basis, this report
examines the Sina weibo, collects the user characteristics and
personality characteristics of weibo users, defines the
forecasting model, analyzes and predicts the personality of
the weibo user, and checks the model's feasibility.
The chart reports the number of papers per year with the
word “personality” in their title (sum over IEEE Explore and
ACM Digital Library).
II. LITERATURE SURVEY
There is an increasing number of research papers in social
networks linked to the actions of a consumer that has
recently gained more interest in the international research
community. Two key fields research the understanding of
personality: computational linguistics and the evaluation of
social networks. Pennebaker and King from the field of
computational linguistics in 1999[14] published a pioneering
work devoted to the retrieval of personality from text. In a
variety of fields such as diaries, college writing tasks, and
social psychology papers, they analyzed terms to analyze
personality-related features with linguistic references. Their
results show that pleasant people prefer to use more terms
whereas introverts and low-consciousness people use more
words to identify themselves. Neurotics are using more
expressions of negative emotion. Argamon et al. 2005[15]
identified neuroticism and extraversion using linguistic
elements such as terms of action, expressions of judgment
and assessment, and verbs of modality. Their findings
showed that neuroticism is related to the use of functional
lexical features, such as lexical taxonomy evaluation,
whereas the results for extraversion were less evident. Many
studies linked neuroticism to irrational beliefs or weak
coping efforts on personality well-being [16] and Nowson
2006 categorized bloggers' extraversion, resilience,
reliability, and conscientiousness using the Naive Bayes
prediction model as a learning algorithm using various sets of
n-grams as features. Mairesse et al. 2007[17] investigated
correlations between the characteristics of the Big 5, using
LIWC and RMC as feature sets. While LIWC features
included word identification such as positive feelings or
frustration, RMC features included word age results or word
imageability results. Mairesse et al. developed a controlled
program of personality identification using the corpus of
essays written by Pennebaker and King in 1999. Of Social
Network Research, there is an even short history of
personality identification from network setup and other extra-
linguistic signals. Gosling et al. studied the effect of the
social interaction behavior of a user on personality [21].
They studied personality traits from self-reported use of
Facebook and observable information on the profile. Rather
than just emotional properties, all interface interfaces are
based on statistical characteristics. Bacharach et al. [22]
found that from their Facebook profiles, people can judge the
personalities of others. Golbeck and so on. [23] Forecast the
personality of 279 Facebook users using language features
such as word count and social media site features such as
counting friends. Orr and others, 2009 [24] showed a positive
correlation between shyness and time spent online and a
negative correlation with the number of friends. Sumner et
al.[25] established the connection between the personalities
of users and their use of Facebook, sharing material and
emotion. Their result showed that transparency is positively
associated with words that express negative feelings, rage,
tabo subjects, income, religion, or death. 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

portions of egocentric network features can be clarified by
psychological predispositions. Personality prediction based
on vocabulary characteristics has been highly interested in
prediction research [27]–[29], but from a network
perspective, the role of links in supporting personality
relationships is not yet well recognized.
Our research aims to investigate the existence of a social
media structure and linguisticcharacteristics linked to
personality experiences using the myPersonality project
dataset [30].
III. DATASETS
We used the myPersonality dataset as a case study to analyze
the personality traits of social media. We built the analysis fr
om the myPersonality survey with 250 users and 9917status
updates.Facebook users ' dataset was named in keeping with t
he Big 5 template. According to the personality styles distrib
ution in Table 3, multiple posts are collected in one folder by
each client in the dataset [30].
 TABLE:
Distribution of Personality Traits
value o c e a n
Yes 176 130 96 134 99
No 74 120 154 116 151
 Note:-
O, C, E, A, N applies to Facebook's distribution of perso
nality ratings on the Big Five traits: openness, intelligenc
e, extraversion, congeniality, and neuroticism.
We picked user information such as user social network
layout, user profile, and text messages with some of the
dataset. The final dataset contains the raw text Facebook
status, author information, personality labels (scores and
classes) and five user social media network metrics inferred
by personality characteristics, i.e. network size, betweeness,
distance, brokerage, and transitivity.
IV. DISCUSSION
Through the current academic findings, we can find that
there is some connection between the social network and the
personality of the user, but the use of social media sites tothe
personality of the survey results of the user is relatively
small. This paper examines the Sinaweibo on the basis of
these, extracts the user's characteristics and personality
characteristics of weibousers, develops the predictive model,
examines and predicts the personality of the weibo user and
verifies the feasibility of the model.
V. CONCLUSION
Based on the existing research, we focus on the
characteristics of the social network and user behavior and
set up three models for comparative analysis. We found that
the behavioral characteristics of the user and their personality
are related by studying social network data can effectively
predict the personality characteristics of the user, which we
can concentrate on the characteristics of social network and
user activity on the basis of existing research, and set up
three predictive models for comparative analysis. We found
that, through the analysis of social network data, the
behavioral characteristics of the user and their personality are
related can be largely predictive of the personality
characteristics of the user, which can urtherly facilitate the
creation of customized applications. Use AI, more accurate
framework for personality prediction can be built in the
future.
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