[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123841-en":3,"doc-seo-123841-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123841,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","Personality Prediction based on Myers Briggs type Indicator Using Machine Learning","This study predicts users’ Myers-Briggs Type Indicator (MBTI) personality types from social media posts by combining machine learning classification/regression models with natural language processing. MBTI is used to map text-derived signals to one of sixteen personality types. The workflow preprocesses textual data through tokenization, regular expressions, lemmatization, sentiment analysis, and part-of-speech tagging, then applies dimensionality reduction. Multiple models are evaluated, with logistic regression achieving the best accuracy, and the trained model is deployed via a web page connected to a Flask application.","Personality Prediction based on Myers Briggs type Indicator Using Machine Learning  \nProf. Ankita Gandhi 1, Vinay Talaviya2*, Lakshmikrishnasai.Alle3, Tanu Yadav4, Abhishek Yadav5  \n1Deputy HOD & Assistant Professor,  \nComputer Science & Engineering Department,  \nParul Institute of Engineering & Technology,  \nParul University, Vadodara, India  \nE-mail: [ankita.gandhi@paruluniversity.ac.in](ankita.gandhi@paruluniversity.ac.in)  \n2*Student, Computer Science & Engineering Department,  \nParul Institute of Engineering & Technology,  \nParul University, Vadodara, India  \n[E-mail: vinaytalaviya4955@gmail.com](E-mail: vinaytalaviya4955@gmail.com)  \n3Student, Computer Science & Engineering Department,  \nParul Institute of Engineering & Technology,  \nParul University, Vadodara, India  \nE-mail: [lakshmikrishnasai2003@gmail.com](lakshmikrishnasai2003@gmail.com)  \n4Student, Computer Science & Engineering Department,  \nParul Institute of Engineering & Technology,  \nParul University, Vadodara, India  \nE-mail: [tanu2681@gmail.com](tanu2681@gmail.com)  \n5Student, Computer Science & Engineering Department,  \nParul Institute of Engineering & Technology,  \nParul University, Vadodara, India  \nE-mail: [a20hekyadav@gmail.com](a20hekyadav@gmail.com)  \nAbstract—In this study, we leverage a combination of machine learning algorithms, including classification and regression models, along with natural language processing techniques, such as NLP and spacy, to predict user personality types from their social media posts. We focus on utilizing the Myers-Briggs Type Indicator (MBTI) to identify a user's unique personality among sixteen possible types [1] . This research aims to establish a correlation between individuals' social media content and their personality traits. Our approach involves extensive preprocessing of textual data, employing techniques like text tokenization, regular expressions, lemmatization, sentiment analysis, and part-ofspeech tagging, followed by dimensionality reduction [2] . We evaluate several machine learning models, including logistic regression, SVM, Naive Bayes, lasso regression, and random forest classifiers, with logistic regression delivering the most accurate results. We deploy this trained model on a web page connected to a Flask app, allowing users to input a brief description of themselves and receive their predicted personality type. This research explores the intersection of text analysis and personality prediction, shedding light on the hidden dimensions of human personality revealed through digital traces in the age of social media [4] .  \nKeywords-Natural Language Processing (NLP), Textual Data Preprocessing, Tokenization, Personality  \nI. INTRODUCTION  \nIn recent years, natural language processing and machine learning advancements have made it possible to predict MBTI personality types from textual data, such as social media posts, essays, or emails. This innovative method employs machine learning algorithms to estimate a person's MBTI personality type by analyzing linguistic patterns [12] .  \nThe Myers-Briggs Type Indicator (MBTI) is a well-known framework for categorizing individuals into 16 personality types, providing valuable insights into their motivations, behaviors, and communication preferences [9] . In this research, we explore various NLP techniques to understand verbal clues indicative of different personality types, shedding light on how  \nFigure 1: 16 Personality Types  \nthese types influence individual traits and behaviors.  \nII. LITERATURE  \nThe Myers-Briggs type indicator is a well-known personality evaluation tool that identifies individual features and groups them into one of the 16 different personality types. However, it would be much more beneficial to design a system that allows users to forecast their personality type. Knowing someone’s  \nMBTI type can be extremely insightful into how they behave, communicate, and make decisions. However, because the MBTI is normally administered using a que","cbCaitvg5rDHovsD","https://ap.wps.com/l/cbCaitvg5rDHovsD","pdf",981258,1,7,"English","en",105,"# Abstract\n# Introduction\n# Literature\n# Methodology\n## Data Source\n## Preparation of Data\n## Cleaning the Data\n## Tokenization","[{\"question\":\"How does the approach predict MBTI personality types from text?\",\"answer\":\"It uses NLP to extract linguistic signals from social media posts and feeds the processed features into machine learning models to classify one of the 16 MBTI types.\"},{\"question\":\"What preprocessing steps are applied to the dataset?\",\"answer\":\"The pipeline includes cleaning via regular expressions (whitespace, removing mail and punctuation, stop words removal), dropping short tokens, plus tokenization, lemmatization, sentiment analysis, and part-of-speech tagging.\"},{\"question\":\"Which machine learning model performs best and how is it delivered to users?\",\"answer\":\"Logistic regression delivers the most accurate results. The trained model is deployed through a web page integrated with a Flask app for user input and predicted output.\"}]","Personality Prediction based on Myers Briggs type Indicator Using Machine Learning | PDF",1785818834,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"personality-prediction-based-on-myers-briggs-type-indicator-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/personality-prediction-based-on-myers-briggs-type-indicator-using-machine-learning/123841/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the approach predict MBTI personality types from text?","Question",{"text":75,"@type":76},"It uses NLP to extract linguistic signals from social media posts and feeds the processed features into machine learning models to classify one of the 16 MBTI types.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What preprocessing steps are applied to the dataset?",{"text":80,"@type":76},"The pipeline includes cleaning via regular expressions (whitespace, removing mail and punctuation, stop words removal), dropping short tokens, plus tokenization, lemmatization, sentiment analysis, and part-of-speech tagging.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best and how is it delivered to users?",{"text":84,"@type":76},"Logistic regression delivers the most accurate results. 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