Understanding people’s emotions and orientations attracts researchers nowadays. Current personality detection research concentrates on models such as the big five model, the three-factor model. The Enneagram is deeper than these models for providing a comprehensive view. This theory is a unique personality model because it illustrates what drives human behavior. This recognition helps in building smarter recommendation systems and intelligent educational systems. Enneagram personalities are realized through a long questionnaire-based test. People are not concerned about doing a test because it is time-consuming. A proposed case study employs Twitter’s text to detect Enneagram personality because it requires no time or effort. The proposed case study is based on an approach that uses a combination of ontology, lexicon, and statistical technique. This proposed case study uses the biography description text and 40 tweets of a Twitter profile text. The highest probability percentage is peacemaker personality which is 15.58%. This result means that the identified personality is the peacemaker. The outcome is equivalent to the determination of the Enneagram’s specialized people. This result promises more positive outcomes. This is the first automated approach to determine the Enneagram from text.
IRJET- Personality Recognition using Social Media DataIRJET Journal
This document summarizes a research paper on personality recognition using social media data. The paper proposes analyzing personality traits based on the Big Five model (openness, conscientiousness, extraversion, agreeableness, neuroticism) using Facebook status updates and machine learning. Specifically, it involves collecting Facebook status data through a browser extension, storing it in a database, training a random forest regression model on an existing dataset, and using the model to predict personality trait values for additional Facebook users. The predicted traits are then visualized with radar charts to provide an overview of each user's personality profile.
This document discusses analyzing human personality characteristics from social media profiles using machine learning techniques. It collects over 3,000 profile summaries from LinkedIn using web scraping. It then uses the WordNet lexical database to form word clusters for extroversion and introversion characteristics. The k-nearest neighbors algorithm is used to classify profiles as extrovert, introvert, or ambivert based on the words in their summaries. The goal is to analyze users' profiles and provide guidance to write profiles that include a balance of introvert and extrovert traits classified as ambivert, which tends to be viewed more positively. Accuracy results from the classification approach are promising but not discussed in detail.
This document discusses using text mining of online social networks to classify user personality traits. It examines using status updates from Facebook profiles as text data to extract features and classify personalities according to the Big Five model using an RBF neural network. The results showed RBF neural networks achieved higher precision than other common classifiers like SVM and Naive Bayes.
This document reviews research on predicting personality from Twitter users' tweets using machine learning algorithms. It discusses how tweets have attracted research interest from diverse fields. Different techniques have been used to predict personality from tweets, but there are still shortcomings to address. The aim is to consider the current state of this research area and explore personality prediction from tweets by reviewing past literature and discussing approaches to issues researchers face. It provides an overview of machine learning methods used for personality prediction from tweets, including data collection, preprocessing, model training and evaluation.
IRJET - Unauthorized Terror Attack Tracking System using Web Usage MiningIRJET Journal
This document describes a system for tracking unauthorized terror attacks using web usage mining and sentiment analysis of social media data. It discusses collecting tweets on a topic using keywords, preprocessing the tweets, analyzing the sentiment of each tweet using TextBlob and VADER sentiment analysis tools, and visualizing the results through graphs and tables. The system aims to help detect terror-related activities by analyzing opinions and sentiments expressed on social media platforms like Twitter.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
This document summarizes a research paper that proposes using a logistic regression classifier trained with stochastic gradient descent to predict Twitter users' personalities from their tweets. It begins with an abstract of the paper and an introduction on personality prediction from social media. It then provides more detail on the anatomy of the research, including defining personality prediction from Twitter, its applications, and the general process of using machine learning for the task. Next, it reviews several previous studies on personality prediction from Twitter and social networks, noting their approaches, findings and limitations. It identifies remaining research gaps, such as the need for improved linguistic analysis of tweets and more robust/scalable predictive models. Finally, it proposes using a logistic regression classifier as the personality prediction model to address
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction
.
IRJET- Personality Recognition using Social Media DataIRJET Journal
This document summarizes a research paper on personality recognition using social media data. The paper proposes analyzing personality traits based on the Big Five model (openness, conscientiousness, extraversion, agreeableness, neuroticism) using Facebook status updates and machine learning. Specifically, it involves collecting Facebook status data through a browser extension, storing it in a database, training a random forest regression model on an existing dataset, and using the model to predict personality trait values for additional Facebook users. The predicted traits are then visualized with radar charts to provide an overview of each user's personality profile.
This document discusses analyzing human personality characteristics from social media profiles using machine learning techniques. It collects over 3,000 profile summaries from LinkedIn using web scraping. It then uses the WordNet lexical database to form word clusters for extroversion and introversion characteristics. The k-nearest neighbors algorithm is used to classify profiles as extrovert, introvert, or ambivert based on the words in their summaries. The goal is to analyze users' profiles and provide guidance to write profiles that include a balance of introvert and extrovert traits classified as ambivert, which tends to be viewed more positively. Accuracy results from the classification approach are promising but not discussed in detail.
This document discusses using text mining of online social networks to classify user personality traits. It examines using status updates from Facebook profiles as text data to extract features and classify personalities according to the Big Five model using an RBF neural network. The results showed RBF neural networks achieved higher precision than other common classifiers like SVM and Naive Bayes.
This document reviews research on predicting personality from Twitter users' tweets using machine learning algorithms. It discusses how tweets have attracted research interest from diverse fields. Different techniques have been used to predict personality from tweets, but there are still shortcomings to address. The aim is to consider the current state of this research area and explore personality prediction from tweets by reviewing past literature and discussing approaches to issues researchers face. It provides an overview of machine learning methods used for personality prediction from tweets, including data collection, preprocessing, model training and evaluation.
IRJET - Unauthorized Terror Attack Tracking System using Web Usage MiningIRJET Journal
This document describes a system for tracking unauthorized terror attacks using web usage mining and sentiment analysis of social media data. It discusses collecting tweets on a topic using keywords, preprocessing the tweets, analyzing the sentiment of each tweet using TextBlob and VADER sentiment analysis tools, and visualizing the results through graphs and tables. The system aims to help detect terror-related activities by analyzing opinions and sentiments expressed on social media platforms like Twitter.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
This document summarizes a research paper that proposes using a logistic regression classifier trained with stochastic gradient descent to predict Twitter users' personalities from their tweets. It begins with an abstract of the paper and an introduction on personality prediction from social media. It then provides more detail on the anatomy of the research, including defining personality prediction from Twitter, its applications, and the general process of using machine learning for the task. Next, it reviews several previous studies on personality prediction from Twitter and social networks, noting their approaches, findings and limitations. It identifies remaining research gaps, such as the need for improved linguistic analysis of tweets and more robust/scalable predictive models. Finally, it proposes using a logistic regression classifier as the personality prediction model to address
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction
.
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...ijcsit
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
Survey on personality predication methods using AIIJAEMSJORNAL
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).
This document outlines a proposal for a Master's thesis on aspect level sentiment analysis for the Arabic language. The proposal was submitted by Mahmoud El Razzaz to Cairo University's Institute of Statistical Studies and Research in fulfillment of an M.Sc. in computer science under the supervision of Prof. Dr. Hesham Hefny and Dr. Mohamed Farouk. The proposal provides an introduction to sentiment analysis and opinion mining, defines the research problem as developing an automated sentiment classification system for Arabic, reviews previous related work, and outlines a work plan to collect and preprocess data, analyze sentiment at different levels, propose an aspect-level approach, test the approach, and report conclusions.
User Personality Prediction on Facebook Social Media using Machine Learningijtsrd
In recent years, Social network use is increasingly build up. The various statistics are split widely through social media Such as Facebook, Twitter. Data about the person and what they communicate through the status updates are important for research in human personality. This paper intends to scrutinize the forecasting of personality traits of Facebook users bases on machine learning and part of the Big ve model this experiment uses my personality data set of Facebook users are used for linguistic factors respective to personality correlation. We used the Data Prepossessing concept of data mining after that feature Extraction. Next, we will work on feature selection. The Personality Prediction system built in the XGboosting classi cation model. Poonam L Patil | Dr. S. R. Jadhao "User Personality Prediction on Facebook Social Media using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33414.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-miining/33414/user-personality-prediction-on-facebook-social-media-using-machine-learning/poonam-l-patil
Application of recommendation system with AHP method and sentiment analysisTELKOMNIKA JOURNAL
This document describes a recommendation system that uses the Analytical Hierarchy Process (AHP) method combined with sentiment analysis. The system allows users to input criteria and alternatives, then performs the following steps:
1. Designs the AHP hierarchy with criteria including sentiment analysis values from social media opinions.
2. Extracts opinions from Twitter about each alternative using its API.
3. Performs sentiment analysis on the opinions to classify them as positive, negative, or neutral.
4. Includes the sentiment values in the AHP calculation to determine the best alternative according to user criteria.
5. Checks the consistency of the AHP analysis to ensure valid results.
The result is a system that provides recommendations to users based
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...mlaij
This study aims to introduce a discussion platform and curriculum designed to help people understand how
machines learn. Research shows how to train an agent through dialogue and understand how information
is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy
based on existing research and integrates a wide range of different subject documents into a set of key AI
literacy skills to develop a user-centered AI. This functionality and structural considerations are organized
into a conceptual framework based on the literature. Contributions to this paper can be used to initiate
discussion and guide future research on AI learning within the computer science community.
The document discusses using Twitter tweets to assess personality. It proposes predicting a user's personality using their public tweets on Twitter based on the DISC framework of dominance, influence, steadiness and compliance. It finds that analyzing word frequencies in a user's tweets can indicate which DISC categories are most dominant. The study aims to develop a useful profiling tool and discusses how personality assessment could benefit fields like marketing, IT and anthropology. It concludes by noting this is an initial attempt and future work could expand the analysis by including location data and interest parameters.
A Review: Text Classification on Social Media DataIOSR Journals
This document provides a review of different classifiers used for text classification on social media data. It discusses how social media data is often unstructured and contains users' opinions and sentiments. Various machine learning algorithms can be used to classify this social media text data, extracting meaningful information. The document focuses on describing Naive Bayes classifiers, which are commonly used for text classification tasks. It explains how Naive Bayes classifiers work by calculating the posterior probability that a document belongs to a certain class, based on applying Bayes' theorem with an independence assumption between features.
This document provides a review of different classifiers used for text classification on social media data. It discusses how social media data is often unstructured and contains users' opinions and sentiments. Various machine learning algorithms can be used to classify this social media text data, extracting meaningful information. The document focuses on describing Naive Bayes classifiers, which are commonly used for text classification tasks. It explains how Naive Bayes classifiers work by calculating the posterior probability that a document belongs to a certain class, based on applying Bayes' theorem with an independence assumption between features.
Predicting user behavior using data profiling and hidden Markov modelIJECEIAES
Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics
Innovating Pedagogical Practices through Professional Development in Computer Science Education
Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data
Enhancing Human-Machine Interaction: Real-Time Emotion Recognition through Speech Analysis
Expert Review on Usefulness of an Integrated Checklist-based Mobile Usability Evaluation Framework
A scalable, lexicon based technique for sentiment analysisijfcstjournal
Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased
interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much
social media available on the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data
available now a days. The main focus of the research was to find such a technique that can efficiently
perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large
data set of tweets using Hadoop and the performance of the technique was measured in form of speed and
accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
Understanding_Users_and_Tasks_CA1_N00147768Stephen Norman
This document is a paper submitted by Stephen Norman for his MSc in User Experience Design program. The paper discusses and compares three user experience evaluation methods - ethnographic field studies, card sorting, and A/B testing. It presents these methods within a three dimensional framework of qualitative vs. quantitative, attitudinal vs. behavioral, and context of use. The paper then discusses how research goals should align with different stages of a product's development lifecycle and be informed by the appropriate evaluation methods.
Personality Prediction with social media using Machine LearningIRJET Journal
This document discusses predicting personality traits using machine learning models trained on social media data. It begins with an abstract describing previous research on using social media data for personality prediction. It then discusses extracting personality-related features from social media posts and using those features to train classifiers like random forests, logistic regression, and KNN. The document outlines collecting and preprocessing Twitter and Facebook data, extracting features, building classification models, and evaluating performance on predicting traits like extraversion and openness. In the end, it discusses challenges in personality prediction and opportunities for future work.
Personality Prediction with social media using Machine LearningIRJET Journal
This document discusses predicting personality traits using machine learning models trained on social media data. It begins with an abstract describing previous research on using social media data for personality prediction. It then discusses extracting features from social media data like tweets and comments using techniques like Myers-Briggs Type Indicator. Various machine learning algorithms are explored for classification, including random forest, logistic regression, and KNN. The methodology section outlines the steps of data collection, feature extraction, model generation and evaluation. In summary, the document proposes using machine learning to predict personality types from social media data by extracting features and training classification models.
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
This document provides an overview of different research methodologies discussed in the context of a feminist perspective. It discusses three main feminist methodologies: feminist empiricism, standpoint epistemology, and post-modernism. The key similarities between these methodologies are a dedication to understanding women's lives and perspectives, moving beyond a masculine focus, and incorporating both men's and women's voices to advance knowledge. The document also briefly outlines some differences between the methodologies in terms of their relationship between experience and theory.
Application For Sentiment And Demographic Analysis Processes On Social MediaWhitney Anderson
This document summarizes a research study on analyzing sentiment and demographics from social media data. The study used machine learning algorithms like N-gram modeling and Naive Bayes classification to analyze tweets and determine if they expressed positive or negative sentiment. Over 350,000 tweets were analyzed from Twitter using these methods. Real-time sentiment analysis of tweets in Turkish was also performed to classify tweets as positive or negative based on n-gram values. The results of this social media analysis can provide useful insights for companies to understand customer sentiment and make informed marketing decisions.
Online social network analysis with machine learning techniquesHari KC
This document summarizes a student's final project presentation on analyzing social networks using machine learning techniques. The objectives of the project were to determine features and sentiment from tweets using natural language processing, evaluate machine learning classifiers' accuracy, and compare the popularity of two smartphones. The methodology involved extracting tweets from Twitter using its API, selecting features for analysis, training and testing classifiers, and calculating sentiment indexes and relative strengths to determine popularity. Results showed classifiers' accuracies, important features, sentiment analysis plots comparing the two smartphones, and that Android had a higher sentiment index and relative strength than iOS based on Twitter data analysis.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...ijcsit
Social networking sites are a significant source of information to know the behavior of users and to know
what is occupying society of all ages and accordingly helpful information can be provided to specialists
and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The
study and analysis of social media data are done to provide the necessary information to increase
investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people
occupy on the communication sites through their tweets about the labor market and investment. Given the
huge volume of data and also its randomness, a survey of the data will be done and collected from through
keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study
analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
Survey on personality predication methods using AIIJAEMSJORNAL
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).
This document outlines a proposal for a Master's thesis on aspect level sentiment analysis for the Arabic language. The proposal was submitted by Mahmoud El Razzaz to Cairo University's Institute of Statistical Studies and Research in fulfillment of an M.Sc. in computer science under the supervision of Prof. Dr. Hesham Hefny and Dr. Mohamed Farouk. The proposal provides an introduction to sentiment analysis and opinion mining, defines the research problem as developing an automated sentiment classification system for Arabic, reviews previous related work, and outlines a work plan to collect and preprocess data, analyze sentiment at different levels, propose an aspect-level approach, test the approach, and report conclusions.
User Personality Prediction on Facebook Social Media using Machine Learningijtsrd
In recent years, Social network use is increasingly build up. The various statistics are split widely through social media Such as Facebook, Twitter. Data about the person and what they communicate through the status updates are important for research in human personality. This paper intends to scrutinize the forecasting of personality traits of Facebook users bases on machine learning and part of the Big ve model this experiment uses my personality data set of Facebook users are used for linguistic factors respective to personality correlation. We used the Data Prepossessing concept of data mining after that feature Extraction. Next, we will work on feature selection. The Personality Prediction system built in the XGboosting classi cation model. Poonam L Patil | Dr. S. R. Jadhao "User Personality Prediction on Facebook Social Media using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33414.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-miining/33414/user-personality-prediction-on-facebook-social-media-using-machine-learning/poonam-l-patil
Application of recommendation system with AHP method and sentiment analysisTELKOMNIKA JOURNAL
This document describes a recommendation system that uses the Analytical Hierarchy Process (AHP) method combined with sentiment analysis. The system allows users to input criteria and alternatives, then performs the following steps:
1. Designs the AHP hierarchy with criteria including sentiment analysis values from social media opinions.
2. Extracts opinions from Twitter about each alternative using its API.
3. Performs sentiment analysis on the opinions to classify them as positive, negative, or neutral.
4. Includes the sentiment values in the AHP calculation to determine the best alternative according to user criteria.
5. Checks the consistency of the AHP analysis to ensure valid results.
The result is a system that provides recommendations to users based
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...mlaij
This study aims to introduce a discussion platform and curriculum designed to help people understand how
machines learn. Research shows how to train an agent through dialogue and understand how information
is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy
based on existing research and integrates a wide range of different subject documents into a set of key AI
literacy skills to develop a user-centered AI. This functionality and structural considerations are organized
into a conceptual framework based on the literature. Contributions to this paper can be used to initiate
discussion and guide future research on AI learning within the computer science community.
The document discusses using Twitter tweets to assess personality. It proposes predicting a user's personality using their public tweets on Twitter based on the DISC framework of dominance, influence, steadiness and compliance. It finds that analyzing word frequencies in a user's tweets can indicate which DISC categories are most dominant. The study aims to develop a useful profiling tool and discusses how personality assessment could benefit fields like marketing, IT and anthropology. It concludes by noting this is an initial attempt and future work could expand the analysis by including location data and interest parameters.
A Review: Text Classification on Social Media DataIOSR Journals
This document provides a review of different classifiers used for text classification on social media data. It discusses how social media data is often unstructured and contains users' opinions and sentiments. Various machine learning algorithms can be used to classify this social media text data, extracting meaningful information. The document focuses on describing Naive Bayes classifiers, which are commonly used for text classification tasks. It explains how Naive Bayes classifiers work by calculating the posterior probability that a document belongs to a certain class, based on applying Bayes' theorem with an independence assumption between features.
This document provides a review of different classifiers used for text classification on social media data. It discusses how social media data is often unstructured and contains users' opinions and sentiments. Various machine learning algorithms can be used to classify this social media text data, extracting meaningful information. The document focuses on describing Naive Bayes classifiers, which are commonly used for text classification tasks. It explains how Naive Bayes classifiers work by calculating the posterior probability that a document belongs to a certain class, based on applying Bayes' theorem with an independence assumption between features.
Predicting user behavior using data profiling and hidden Markov modelIJECEIAES
Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
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social media available on the web, sentiment analysis is now considered as a big data task. Hence the
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Personality Prediction with social media using Machine LearningIRJET Journal
This document discusses predicting personality traits using machine learning models trained on social media data. It begins with an abstract describing previous research on using social media data for personality prediction. It then discusses extracting personality-related features from social media posts and using those features to train classifiers like random forests, logistic regression, and KNN. The document outlines collecting and preprocessing Twitter and Facebook data, extracting features, building classification models, and evaluating performance on predicting traits like extraversion and openness. In the end, it discusses challenges in personality prediction and opportunities for future work.
Personality Prediction with social media using Machine LearningIRJET Journal
This document discusses predicting personality traits using machine learning models trained on social media data. It begins with an abstract describing previous research on using social media data for personality prediction. It then discusses extracting features from social media data like tweets and comments using techniques like Myers-Briggs Type Indicator. Various machine learning algorithms are explored for classification, including random forest, logistic regression, and KNN. The methodology section outlines the steps of data collection, feature extraction, model generation and evaluation. In summary, the document proposes using machine learning to predict personality types from social media data by extracting features and training classification models.
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
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A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
This document provides an overview of different research methodologies discussed in the context of a feminist perspective. It discusses three main feminist methodologies: feminist empiricism, standpoint epistemology, and post-modernism. The key similarities between these methodologies are a dedication to understanding women's lives and perspectives, moving beyond a masculine focus, and incorporating both men's and women's voices to advance knowledge. The document also briefly outlines some differences between the methodologies in terms of their relationship between experience and theory.
Application For Sentiment And Demographic Analysis Processes On Social MediaWhitney Anderson
This document summarizes a research study on analyzing sentiment and demographics from social media data. The study used machine learning algorithms like N-gram modeling and Naive Bayes classification to analyze tweets and determine if they expressed positive or negative sentiment. Over 350,000 tweets were analyzed from Twitter using these methods. Real-time sentiment analysis of tweets in Turkish was also performed to classify tweets as positive or negative based on n-gram values. The results of this social media analysis can provide useful insights for companies to understand customer sentiment and make informed marketing decisions.
Online social network analysis with machine learning techniquesHari KC
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
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Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
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A review on features and methods of potential fishing zoneIJECEIAES
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
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findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
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gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
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Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
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generator, and micro-turbine, are controlled. The micro-turbine generator is
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Precisely characterizing Li-ion batteries is essential for optimizing their
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applications, such as electric vehicles and renewable energy systems. This
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Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
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significant interest from the research community. Assessing the field's
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deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
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objectives. First, a bibliometric analysis is presented to give an overview of
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the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
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Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
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negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
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including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
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Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
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system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
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grid. Additionally, a fuzzy logic-based maximum power point tracking
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incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
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IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
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errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
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Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Approach for Enneagram personality detection for Twitter text: a case study
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6984~6991
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6984-6991 6984
Journal homepage: http://ijece.iaescore.com
Approach for Enneagram personality detection for Twitter text:
a case study
Esraa Abdelhamid, Sally S. Ismail, Mostafa Aref
Computer Science Department, Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt
Article Info ABSTRACT
Article history:
Received Mar 6, 2023
Revised May 3, 2023
Accepted Jun 4, 2023
Understanding people’s emotions and orientations attracts researchers
nowadays. Current personality detection research concentrates on models
such as the big five model, the three-factor model. The Enneagram is deeper
than these models for providing a comprehensive view. This theory is a
unique personality model because it illustrates what drives human behavior.
This recognition helps in building smarter recommendation systems and
intelligent educational systems. Enneagram personalities are realized
through a long questionnaire-based test. People are not concerned about
doing a test because it is time-consuming. A proposed case study employs
Twitter’s text to detect Enneagram personality because it requires no time or
effort. The proposed case study is based on an approach that uses a
combination of ontology, lexicon, and statistical technique. This proposed
case study uses the biography description text and 40 tweets of a Twitter
profile text. The highest probability percentage is peacemaker personality
which is 15.58%. This result means that the identified personality is the
peacemaker. The outcome is equivalent to the determination of the
Enneagram’s specialized people. This result promises more positive
outcomes. This is the first automated approach to determine the Enneagram
from text.
Keywords:
Enneagram
Personality detection
Personality model
Sentiment analysis
Text mining
This is an open access article under the CC BY-SA license.
Corresponding Author:
Esraa Abdelhamid
Computer Science Department, Faculty of Computer and Information Science, Ain Shams University
Abbasia, Cairo, Egypt
Email: esraa_cs@hotmail.com
1. INTRODUCTION
Personality detection attracts researchers nowadays. The availability of huge volumes of data in
fields like psychology, text mining, machine learning, and natural language processing motivates academics
to conduct studies to discover people’s sentiments [1]. Identifying the personality can benefit many domains.
Recommendation systems are greatly helpful by understanding people’s behavior and preferences because it
enables us to greatly enhance it [2]. Personality helps recognize the person’s directions, likes, and preferences
on social platforms. This knowledge aids in attracting more users and bringing in extra advertisements. The
huge quantity of text created by users has motivated the development of author profiling [3]. Personality
identification aids in selecting the right candidate in the job hiring process. This awareness can give an
insight into the person’s abilities which is compared to the job qualifications.
The personality assessment test is a method to decide the personality. Humans have no enthusiasm
for doing a test because it is time-consuming. People are unwilling to complete a questionnaire since it is
inconvenient and time-consuming [4]. There are other disadvantages to the assessment. The main concern
when conducting a personality test is that responders prefer to answer in a faking manner with artificial
responses [5]. Identifying the personality is a complex mission because it demands the knowledge of the
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specialized. Natural language understanding makes the task more complex. Personality recognition is gaining
popularity in a variety of contexts including social media, robotics, speech processing, and human computer
interaction [6]. Social media is playing a crucial role nowadays. Digital footprints can indicate personality
traits. It can also be used as a quick method of survey with lower fees and larger population support [7]. There
are other benefits of social media. Crowd behavior analysis utilizes social media users’ information [8].
Personality defines the attributes of the person’s characteristics. There are many personality models: the
Myers-Briggs type indicator (MBTI), the three-factor model, the big five model, and Enneagram.
The Enneagram is a personality theory that understands human behavior at a depth level. The
Enneagram is a mapping of human nature’s nine personality types and their mutual relationships [9]. This
personality model consists of nine types: reformer, helper, achiever, individualist, investigator, loyalist,
enthusiast, challenger, and peacemaker. This personality model demonstrates fears, desires, features,
motivations, and problems for the nine personalities. The main advantage of the Enneagram is describing
what drives human behavior. It can teach a person about his/her strengths, weaknesses, and aim for his/her
growth [10]. This personality model is more personal than other models. Many universities in the United
States are studying the Enneagram in psychology, education, medicine, business, and arts [11].
There are many other benefits. It is a useful tool for improving friends, family, and workplace
relationships [12]. It also helps the counselor. It assists the counselor in detecting client behavior which
enhances development and recovery [13]. Psychological assistance is vital in helping a patient’s recovery.
Since 1970, psychiatrists have utilized the Enneagram [14]. Knowing this model as fast as possible aids in
recovery in less time. In education, a useful tool for overall student development is the Enneagram [15].
Personality detection by the analysis of social platforms is a recent research domain in machine learning [16].
There are multiple drawbacks of machine learning in consistency, transparency, and dependability. These
difficulties are especially important in the natural language processing area which prohibits artificial
intelligence from obtaining human-like performance [17].
The Enneagram personality detection approach consists of four main stages: text preprocessing,
feature extraction, feature selection, and personality detection. Text preprocessing purpose is to prepare the
text and remove unimportant information. Feature extraction transforms preprocessed text into word-based
features. Feature selection is to pick the personalities’ relevant features. This stage is performed using the
saurus English lexicon [18] and Enneagram ontology [19], [20] The personality detection phase uses a
statistical technique to calculate the personality’s probability distribution for each one. The largest probability
percentage is the detected personality [21]. The approach uses Twitter Text. Twitter is a large platform that
provides a huge quantity of text. Twitter is a great chance to study naturally the people’s attitudes [22]. The
proposed case study is applied to the public profile Twitter account of Morgan Freeman [23]. Probability
distributions for each personality are calculated and resulted in 15.58%, 12.12%, 11.90%, 11.69%, 11.47%,
11.04%, 9.96%, 9.52%, and 6.71%. The largest percentage is 15.58% among personalities that is the
peacemaker. The result analysis is that the peacemaker is the personality of this profile. This conclusion is
identical to the Enneagram expert’s analysis [24].
This paper contains several sections: the related work, the method, the proposed personality
detection case study, the result, and the conclusion. The related work demonstrates recent work done on
personality detection. The method describes the approach for Enneagram personality. The proposed
personality detection case study demonstrates the details of the proposed case study. The result section
demonstrates the result and the case study positive outcomes. The conclusion section summarizes the
approach, case study, and the result inference with future direction.
2. RELATED WORK
For detecting personality from text, two strategies have been used: a machine learning technique and
text linguistic properties method [25]. Recent research has used different machine learning techniques to
identify personality. Different machine learning techniques are employed like unsupervised estimation and
deep learning. The big five personality model is utilized in the major of the systems. The future directions are
enhancing performance by using other techniques, more features, advanced language processing, testing on
various platforms, more efficient parsing, and better preprocessing techniques as shown in Table 1.
There are several approaches in text for extracting personality characteristics. Several methods employ
a closed-vocabulary technique having psycholinguistic tools, while others employ an open-vocabulary one.
Most personality prediction research requires a dataset to execute supervised learning and obtaining social
media users’ personality traits dataset has a high cost. Present personality prediction can be improved in
many directions like using more appropriate algorithms or preprocessing approaches to obtain greater accuracy
and implementing additional personality models [26]. Artificial recognition of personality from text is a hard
mission. This domain research is still challenging and needs a lot of enhancements.
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Table 1. Related work
Description Technique Personality model Future direction
Personality detection system
was presented that integrates
questionnaire-based and text-
based techniques [27].
Unsupervised estimation Big five
personality
model
• Develop more advanced natural language
processing models.
• Evaluate other systems different from
personality.
Two attention architectures
for incorporating emoji and
textual information in
personality identification
were presented [28].
Deep learning architecture Big five
personality
model
• Incorporate visual features to improve
performance.
• Test models on more social datasets.
• Use other learning models to improve
performance.
Personality identification
system was proposed.
A dictionary system is
developed [29]–[31].
Ontology and five
algorithms (sentence
processing, database saving,
csv processing, radix tree
conversion model, and trait
estimation)
Big five
personality
model
• Use a better parsing algorithm to get
more accurate results.
• Add more words to the platform’s corpus
from various social media platforms.
• Give weights to classified words instead
of the word’s frequency only.
3. METHOD
The applied language is the English language. Google Colab is used as a development platform. The
used programming language is Python. There are many applied libraries: text preprocessing python package,
TextBlob, Keras text tokenizer, OwlReady2 and Tweepy. Text preprocessing step packages are text
preprocessing package, TextBlob and Keras text tokenizer. Ontology access is utilized by the OwlReady2
package. Tweepy is applied to access Twitter. The experiment is performed on 16.0 GB RAM, 64-bit system
type and Intel(R) Core (TM) i7-1065G7 CPU.
Tweets are selected as input to the personality detection approach. The approach utilizes both the
Enneagram ontology [19], [20] and the English Lexicon [18]. Enneagram ontology represents Enneagram
knowledge. The English lexicon enhances the vocabulary seeds. The general approach is shown in Figure 1.
Figure 1. Personality detection model
The personality detection approach composes of several phases: text pre-processing, feature
extraction, feature selection, and personality detection. The text pre-processing target is to remove
unnecessary information from the text. The text pre-processing step includes text cleaning, text
normalization, stemming, and lemmatization. Text cleaning involves the removal of hashtags, Twitter
handlers, numbers, extra space, stop words, uniform resource locators (URLs), emails, punctuation, and
special character. Text normalization composes of checking the spelling, normalizing Unicode, and lower-
case folding. The last one is stemming and lemmatization words. All steps are shown in Figure 2.
Figure 2. Text preprocessing phase
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In feature extraction, this stage’s purpose is to obtain the qualifying features. The text is required to
be converted to features. First, the preprocessed text is tokenized. Second, the tokenization is transformed
into a bag of words. It contains the words as features and their relevant occurrences count.
Feature selection is vital to pick the important features. The Enneagram ontology [19], [20] and the
thesaurus English lexicon [18] are both used in word-based feature selection. Ontology supplies knowledge
of a domain [32]. The English lexicon enhances the vocabulary. It enriches the initial lists with equivalent
words. The new word lists are formed from the initial lists which are extracted from the ontology and the
equivalent words from the English lexicon. Each list is relative to one of the nine personalities. Then, the
nine final word lists are preprocessed. The features are chosen from the pre-processed final words lists that
are found in the bag of words. Nine bags of words are formed from the intersection between nine lists and a
bag of words of the main text as shown in the next Figure 3.
Figure 3. Word-based feature selection
Personality detection is applied to identify the nine distinct personalities. The goal of this stage is to
detect the Enneagram personality. The total occurrences count for each bag of words is computed to determine
the personality distribution. Every personality bag of words contains words that belong to the personality and
count occurrences in the main text. The probability for a personality equals the sum of a personality bag of
words occurrences count divided by the sum of word occurrences count for all personalities. Enneagram’s
personalities probability distribution is computed for each of the nine personalities. The highest probability
distribution for a personality means that it is the detected one [21].
4. PROPOSED PERSONALITY DETECTION CASE STUDY
The proposed case study is applied to the public profile Twitter of Morgan Freeman [23]. The case
study contains the biography description text and 40 tweets. The data are available here [33]. The procedure
for the approach contains four steps. The steps include text pre-processing, feature extraction, feature
selection, and personality detection. In the text pre-processing phase, the unnecessary text is removed. Also,
the text is prepared for the next stage. The description and 40 tweets are preprocessed. The pre-processing
involves removing emails, URLs, hashtags, Twitter handler, punctuations, stop words, extra spaces, special
characters, and numbers. Text preprocessing also consists of lower-case folding, normalizing Unicode,
checking the spelling, stemming, and lemmatization. The preprocessed text is available here. The text is
ready for the next phase.
In the feature extraction phase, this stage’s target is to convert preprocessed text to word-based
features. The preprocessed description text and preprocessed 40 tweets are tokenized. Then, the tokenization
is transformed into a bag of word-based features. The bag of words contains words in the text and the
occurrences count of each of these words. There are many words and their counter occurrences like prison:
12, friend: 7, thank: 6, year: 6, new: 5, love: 4. For example, the love word is found in the preprocessed text
4 times. The details are shown in the next Figure 4.
In the feature selection phase, the objective of this phase is to elect the right candidate features. The
election criteria are based on the Enneagram ontology and the English lexicon. The word list is created from
the Enneagram ontology. Using the English lexicon, the list of words is updated with equivalent words. Then,
the final word list is pre-processed. The features are selected from the bag of words using the final words list.
Personality detection is the last phase. The purpose of this stage is to identify a person’s personality.
The personality’s words count occurrences are calculated for the nine personalities. Enneagram personalities’
probability distributions are computed. The highest probability distribution personality is the desired one.
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The largest percentage distribution is the peacemaker which is 15.58%. This result indicates that the profile’s
personality is the peacemaker. This result matches the determination of the Enneagram’s experts [24].
Figure 4. Case study: bag of words
5. RESULT AND DISCUSSION
Each personality has related words that intersect with the main text’s bag of words. The nine
personalities’ probability distributions are calculated and range from 15.58% to 6.71%. Different
personalities’ probability distributions are peacemaker: 15.58%, individualist: 12.12%, reformer: 11.90%,
achiever: 11.69%, investigator: 11.47%, enthusiast: 11.04%, helper: 9.96%, loyalist: 9.52%, and challenger:
6.71%. Peacemaker is the largest percentage produced. Many features are selected with their count for the
peacemaker like ‘take’, ‘recon’, ‘set’, ‘happi’, ‘pleas’, ‘creat’, ‘hope’. Each selected feature has a count for
each occurrence like ‘take’: 2, ‘recon’: 1,’set’: 2, ‘happi’: 2, ‘pleas’: 2, ‘hope’: 1.
Peacemaker selected features with their count are the following: ‘take’: 2, ‘recon’: 1, ‘set’: 2,
‘happi’: 2, ‘pleas’: 2, ‘disco’: 2, ‘make’: 3, ‘plan’: 1, ‘start’: 1, ‘creat’: 2, ‘relax’: 1, ‘seen’: 1, ‘last’: 1, ‘hope’:
1, ‘help’: 1, ‘cheer’: 1, ‘good’: 1, ‘like’: 2, ‘talent’: 2, ‘back’: 2, ‘inspire’: 2, ‘raise’: 1, ‘continue’: 3, ‘busi’: 2,
‘commit’: 1, ‘friendship’: 1, ‘toget’: 1, ‘tune’: 1, ‘determin’: 1, ‘fail’: 3, ‘fall’: 2, ‘differ’: 1, ‘break’: 7,
‘accord’: 1, ‘love’: 4, ‘meet’: 1, ‘connect’: 1, ‘join’: 7, ‘link’: 1 . The sum of the selected peacemaker features
count is 72. The sum of all personalities selected features count is 462. Peacemaker’s probability distribution
equals 72 divided by 462 so the computed result is 15.58 %. The illustration for each personality, selected
words-based features with count and the probability distribution percentage are shown in the Table 2.
The highest probability distribution is the peacemaker personality with 15.58%. The result states
that the profile personality is a peacemaker. The outcome matches the Enneagram experts’ analysis [24]. This
result drives us to the conclusion that the approach can determine the Enneagram’s personality. The
integration of the Enneagram ontology, English lexicon, and statistical technique are qualified for detecting
personality. The author’s profile text gives the direction of his/her personality.
Current research on personality detection utilizes the MBTI, the big five model, and the three-factor
model. Enneagram is more difficult and deeply perceives the personality. Other personality models assess
specific traits without providing insight into behavior’s motivation. The Enneagram not only outlines all
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personality characteristics but also provides every personality’s deeply rooted patterns of thoughts, feelings,
and behaviors [34]. This is the first Enneagram personality detection approach. This approach encourages
more research in Enneagram personality detection. In the future, applying Enneagram personality
identification will lead to many benefits. This minimizes time and effort for knowing their personality.
Table 2. Morgan Freeman’s case study result
Personality type Selected features Probability percentage
Peacemaker ‘take’: 2, ‘recon’: 1, ‘set’: 2, ‘happi’: 2, ‘pleas’: 2, ‘disco’: 2, ‘make’: 3, ‘plan’: 1, ‘start’: 1,
‘creat’: 2, ‘relax’: 1, ‘seen’: 1, ‘last’: 1, ‘hope’: 1, ‘help’: 1, ‘cheer’: 1, ‘good’: 1, ‘like’: 2,
‘talent’: 2, ‘back’: 2, ‘inspire’: 2, ‘raise’: 1,’continue’: 3, ‘busi’: 2, ‘commit’: 1, ‘friendship’: 1,
‘toget’: 1, ‘tune’: 1, ‘determin’: 1, ‘fail’: 3, ‘fall’: 2, ‘differ’: 1, ‘break’: 7, ‘accord’: 1,
‘love’: 4, ‘meet’: 1, ‘connect’: 1, ‘join’: 7, ‘link’: 1
15.58%
Individualist ‘beauty’: 1, ‘commun’: 1, ‘reveal’: 1, ‘say’: 2, ‘tell’: 1, ‘voice’: 1, ‘compass’: 2, ‘draw’: 2,
‘like’: 2, ‘pictur’: 1, ‘portrait’: 2, ‘state’: 1, ‘man’: 2, ‘parti’: 1, ‘air’: 2, ‘charact’: 1,
”humor’: 1, ‘wish’: 2, ‘recon’: 1, ‘release’: 1,’disco’: 2, ‘make’: 3, ‘plan’: 1, ‘ ‘start’: 1,
‘creat’: 2, ‘spur’: 1, ‘inspire’: 2, ‘continue’: 3, ‘play’: 1, ‘theater’: 1,’authen’: 1, ‘honor’: 1,
‘since’: 2, ‘embarrass’: 1, ‘believ’: 1, ‘think’: 2, ‘nature’: 1, ‘moment’: 1, ‘differ’: 1
12.12%
Reformer ‘differ’: 1, ‘join’: 7, ‘link’: 1, ‘great’: 2, ‘wonder’: 1,’talent’: 2, ‘help’: 1, ‘good’: 1, ‘honor’: 1,
‘line’: 1, ‘plan’: 1,’system’: 1, ‘tell’: 1, ‘creat’: 2, ‘simple’: 1, ‘truth’: 1, ‘determin’: 1, ‘project’:
2, ‘wish’: 2, ‘better’: 1, ‘correct’: 3,’raise’: 1, ‘recon’: 1, ‘happi’: 2, ‘busi’: 2, ‘ill’: 2, ‘fall’: 2,
‘impose’: 1, ‘authen’: 1, ‘since’: 2, ‘war’: 2, ‘success’:1, ‘continue’: 3, ‘cheer’: 1
11.90%
Achiever ‘honor’: 1, ‘treasure’: 1, ‘think’: 2, ‘determin’: 1, ‘recon’: 1, ‘disco’: 2, ‘see’: 3, ‘inspire’: 2,
‘authen’: 1, ‘spur’: 1,’draw’: 2, ‘like’: 2, ‘pictur’: 1, ‘portrait’: 2, ‘state’: 1, ‘hope’: 1, ‘love’: 4,
‘wish’: 2, ‘bring’: 2, ‘kill’: 1, ‘send’: 1,’play’: 1, ‘beauty’: 1, ‘magic’: 1, ‘pleas’: 2, ‘effort’: 2,
‘for’: 2, ‘make’: 3, ‘fly’: 1, ‘guid’: 1, ‘start’: 1, ‘service’:1, ‘seen’: 1, ‘wait’: 1, ‘courage’: 1,
‘favorit’: 1
11.69%
Investigator ‘determin’: 1, ‘explore’: 2, ‘recon’: 1, ‘reveal’: 1, ‘see’: 3, ‘disco’: 2, ‘pictur’: 1, ‘war’: 2,
‘watch’: 1, ‘wonder’: 1, ‘plan’: 1, ‘start’: 1, ‘great’: 2, ‘find’: 1, ‘nail’: 1, ‘good’: 1, ‘talent’: 2,
‘play’: 1, ‘back’: 2, ‘conside’: 1, ‘break’: 7,’tell’: 1, ‘guess’: 1, ‘include’: 1, ‘project’: 2,
‘call’: 1, ‘think’: 2, ‘ill’: 2, ‘service’: 1, ‘serv’: 2, ‘help’: 1, ‘need’: 1, ‘work’: 3
11.47%
Enthusiast ‘believ’: 1, ‘don’t’: 1, ‘work’: 3, ‘busi’: 2, ‘game’: 1, ‘set’: 2, ‘take’: 2, ‘play’: 1, ‘system’: 1,
‘simple’: 1, ‘talent’:2, ‘enjoy’: 2, ‘recon’: 1, ‘honor’: 1, ‘like’: 2, ‘love’: 4, ‘treasure’: 1,
‘meet’: 1, ‘focus’: 1, ‘cheer’: 1, ‘humor’:1, ‘wonder’: 1, ‘happi’: 2, ‘pleas’: 2, ‘chang’: 1,
‘charact’: 1, ‘nature’: 1, ‘rob’: 1, ‘ill’: 2, ‘shock’: 1, ‘amaz’: 2,’event’: 1, ‘avail’: 1,
‘release’: 1, ‘success’: 1, ‘help’: 1
11.04%
Helper ‘enjoy’: 2, ‘recon’: 1, ‘honor’: 1, ‘like’: 2, ‘love’: 4, ‘treasure’: 1,’dear’: 1, ‘relax’: 1,
‘meet’: 1, ‘commun’: 1,’reveal’: 1, ‘say’: 2, ‘tell’: 1, ‘voice’: 1, ‘service’: 1, ‘back’: 2,’serv’: 2,
‘help’: 1, ‘commit’: 1, ‘wish’: 2, ‘want’: 2,’need’: 1, ‘effort’: 2, ‘watch’: 1, ‘determin’: 1,
‘compass’: 2, ‘good’: 1, ‘cheer’: 1, ‘pleas’: 2, ‘since’: 2, ‘fond’: 1, ‘friendship’: 1
9.96%
Loyalist ‘send’: 1, ‘commit’: 1, ‘help’: 1, ‘join’: 7, ‘meet’: 1, ‘fall’: 2, ‘busi’: 2, ‘determin’: 1, ‘for’: 2,
‘quick’: 1, ‘doubt’:1, ‘war’: 2, ‘believ’: 1, ‘conside’: 1, ‘think’: 2, ‘wonder’: 1, ‘correct’: 3,
‘hope’: 1, ‘cheer’: 1, ‘inspire’: 2, ‘super’:1, ‘last’: 1, ‘take’: 2, ‘back’: 2, ‘raise’: 1, ‘continue’: 3
9.52%
Challenger ‘say’: 2, ‘meet’: 1, ‘accord’: 1, ‘determin’: 1, ‘find’: 1, ‘talent’: 2, ‘for’: 2, ‘system’: 1,
‘success’: 1, ‘good’: 1,’courage’: 1, ‘great’: 2, ‘charact’: 1, ‘wish’: 2, ‘impose’: 1, ‘better’: 1,
‘correct’: 3, ‘help’: 1, ‘raise’: 1, ‘recon’: 1, ‘spur’: 1, ‘inspire’: 2, ‘humor’: 1
6.71%
Automated Enneagram personality detection has an impact in different areas like recommendation
systems, psychiatrists, psychologists, education. In recommendation systems, this identification recognizes
the likes and dislikes of the person’s profile which enhances the commercial advertising. Better
recommendation systems that can act in human-like performance. Quick recognition helps the psychiatrist
and psychologist to give the desired assistance to patients in less time. It can also be applied in education to
provide support for students to raise their achievements. Enneagram can enhance postgraduate education,
professional connections, and boost motivation during tough patient care situations [35].
The result cannot demonstrate the level of accuracy of the approach. The case study investigates a
single profile that does not give the big picture. Other personalities are required to explore. More case studies
need to be discussed. Different profiles with different personalities are also needed. Various profiles will be
analyzed to determine the level of accuracy.
6. CONCLUSION
An Enneagram personality identification case study is proposed. The public Twitter profile text is
analyzed which contains a biography description and 40 tweets. The steps of the approach are multiple: text
preprocessing, feature extraction, feature selection, and personality detection. The main goal of text
preprocessing is to clean the text from unimportant and additional text. Feature extraction obtains words from
text as word-based features. The purpose of feature selection is to elect the right features with the
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combination of the Enneagram ontology and the English lexicon. The personality detection phase uses
probability distribution across personalities. The largest value of the probability distributions is the desired
personality.
According to the proposed case study, text pre-processing contains normalization, cleaning,
lemmatization, and stemming. Feature extraction includes tokenization and bag of words representation.
Feature selection is based on the list of words and bag of words. Personalities lists are formed from
Enneagram ontology and English Lexicon. Personality-related features are chosen from the personality-
related list of words and found in the bag of text. Personality detection is applied based on calculating the
personalities’ probability distributions. The probability distributions for each personality from the highest to
the lowest are peacemaker: 15.58%, individualist: 12.12%, reformer: 11.90%, achiever: 11.69%, investigator:
11.47%, enthusiast: 11.04%, helper: 9.96%, loyalist: 9.52%, and challenger: 6.71%. The highest probability
distribution is 15.58%. This percentage belongs to the peacemaker personality. This result inferred that the
profile personality is the peacemaker personality. This result matches the Enneagram experts’ conclusion.
This is the first Enneagram personality detection approach. Past research focused on other
personality models. The Enneagram provides a deeper understanding because it illustrates the person’s
desires, motivations, and fears. The case study result is identical to the Enneagram’s expert analysis. The
output indicates a positive direction. This system can help recommendation systems, education, psychiatrists,
psychologists, and physicians instead of applying the test. This paper is a gate and encourages more
investigation in this domain.
In the future, the approach will be applied to more case studies and results. Another future
enhancement, a system will be developed that can infer the person’s personality in unhealthy and healthy
conditions. Unhealthy conditions may lead to suicide attempts or/and personality disorders. Different
approaches will be implemented to compare the outcomes.
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BIOGRAPHIES OF AUTHORS
Esraa Abdelhamid is a Ph.D. student in the computer science department at the
faculty of computer and information science, Ain Shams University, Cairo, Egypt. She
received her master’s degree in computer science from the faculty of computer and
information science, Ain Shams University in 2016. Her research interests include text mining,
natural language processing, data mining, knowledge representation, and multiagent systems.
She can be contacted at email: esraa_cs@hotmail.com.
Sally S. Ismail holds a Ph.D. in Computer Science field from the University of Ain
Shams, subspecialty in natural language processing. She is currently a lecturer in the Computer
Science Department, Faculty of Computer and Information Sciences, and formerly the
program coordinator in the software engineering program. Her research interests include
information retrieval, text summarization, and sentiment analysis. She can be contacted via
email: Sallysaad@cis.asu.edu.eg.
Mostafa Aref is a professor of Computer Science at Ain Shams University, Cairo,
Egypt. He holds a Ph.D. in engineering science in system Theory and engineering, June 1988,
University of Toledo, Toledo, Ohio. He obtained his M.Sc. in computer science, October 1983,
University of Saskatchewan, Saskatoon, Sask. Canada. He received the B.Sc. of electrical
engineering-computer and automatic control section, in June 1979, electrical engineering
department, Ain Shams University, Cairo, Egypt. His research areas are natural language
processing, knowledge representation, and ontology. He can be contacted at email:
mostafa.m.aref@gmail.com.