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Model comparisons include GRU, LSTM, and RNN under varying hyperparameters such as node numbers, dropout regularization, and training epochs. The best accuracy reaches 0.938, and the analysis evaluates how sentiment features affect performance on tweet prediction, with suggestions for future improvements.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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problem does the paper address?","Question",{"text":63,"@type":64},"The paper predicts MBTI personality types from language data using natural language processing and deep learning models.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which models and hyperparameters are compared?",{"text":68,"@type":64},"It compares GRU, LSTM, and RNN models, varying node numbers, dropout regularization, and the number of epochs.",{"name":70,"@type":61,"acceptedAnswer":71},"How are Azure sentiment scores and social media posts used?",{"text":72,"@type":64},"The study extends the dataset with Azure sentiment scores and social media posts to test how sentiment information influences prediction accuracy, particularly for Tweets.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},293574,1789665828,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & 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 \n015  \n016  \n017  \n018  \n019  \n020  \n021  \n022  \n023  \n024  \n025  \n026  \n027  \n028  \n029  \n030  \n031  \n032  \n033  \n034  \n035  \n036  \n037  \n038  \n039  \n040  \n041  \n042  \n043  \n044  \nPredicting MBTI Personalities based on Language Usage by Natural Language Processing Algorithms  \nTe-Yuan Chen, Yiyan Huang  \nThe University of Texas at Austin  \nAbstract. Using machine learning to predict personalities is not only bene􀀌cial for customizing users' experience of interacting with products, but could further improve their experience of using arti􀀌ciall intelligence products. This paper used natural language processing algorithms to predict MBTI personalities and extend the dataset scope to include Azure sentiment score and social media posts to testify prediction performance.  \nIt compared models of GRU, LSTM and RNN with di􀀋erent hyperparameters of node numbers, dropout regularization and epochs, and found the best prediction accuracy of 0.938 . Meanwhile, it measured the in􀀍uence of Azure sentiment score on prediction accuracy and the e􀀋ectiveness of algorithm in predicting Tweets. Although results 􀀌nd the latter two e􀀋orts futile, the prediction results might be improved in the future by expanding dataset and incorporating more features.  \n1 Introduction  \nMotivation Personality categories are widely used for team building and division of tasks. Applications and products on social networks and team management tools also built their framework based on personality matching too. The application of personality prediction algorithm could also improve user experience with customized information presented, creating segmented markets and attaching characters to AI products with personality traits users are comfortable with. It could also address pain points such as those when users are asked to ful􀀌ll their own personalities, they expressed concerns of agnostics and dishonesty in ful􀀌lling personality.  \nStudies showed high correlation between personality re􀀍ected and the language and text expressed by subjects. Two popular psychological personality assessments are the Big Five and Myers Briggs Type Indicator (MBTI) . The Big Five measures personality with scores whilst MBTI de􀀌nes 4 binary classes extending 4 scales, including Extraversion (E)-Introversion (I), Sensing (S)-Intuition (I), Perception (P) - Judging (J), Feeling (F)-Thinking (T) . Previous studies also showed a higher feasibility of predicting MBTI in comparison with the Big Five. Using MBTI as the framework for measuring personality traits is more suitable for making predictions based on machine learning as well as future application in various products.  \n000  \n001  \n002  \n003  \n004  \n005  \n006  \n007  \n008  \n009  \n010  \n011  \n012  \n013  \n014  \n015  \n016  \n017  \n018  \n019  \n020  \n021  \n022  \n023  \n024  \n025  \n026  \n027  \n028  \n029  \n030  \n031  \n032  \n033  \n034  \n035  \n036  \n037  \n038  \n039  \n040  \n041  \n042  \n043  \n044  \n045  \n046  \n047  \n048  \n049  \n050  \n051  \n052  \n053  \n054  \n055  \n056  \n057  \n058  \n059  \n060  \n061  \n062  \n063  \n064  \n065  \n066  \n067  \n068  \n069  \n070  \n071  \n072  \n073  \n074  \n075  \n076  \n077  \n078  \n079  \n080  \n081  \n082  \n083  \n084  \n085  \n086  \n087  \n088  \n089  \n2 ECCV-16 submission ID ***  \nLiterature Review Previous studies covered using Twitter and other social media to make MBTI predictions [1{4] . The algorithms they used to predict personality included Naive Bayes and Support Vector Machine(SVM)[5, 6] . However, we didn't see papers that use the RNN model to analyze language data and build a model. Meanwhile, we used the Kaggle open dataset with data collected through the Personality Cafe forum, whose MBTI labeling is more accurate because users labeled personalities by themselves. We also found previous studies only used traditional classi􀀌cation methods whilst the RNN model was rarely utilized.  \nTo this end, we will use deep learning algorithms in natural ","cbCair6HJIvtyZXx","https://ap.wps.com/l/cbCair6HJIvtyZXx","pdf",217027,"English","# Abstract\n# 1 Introduction\n## Motivation\n# 2 Related Work\n## Relations between Personality Categorization and Language Use\n# Literature Review","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper predicts MBTI personality types from language data using natural language processing and deep learning models.\"},{\"question\":\"Which models and hyperparameters are compared?\",\"answer\":\"It compares GRU, LSTM, and RNN models, varying node numbers, dropout regularization, and the number of epochs.\"},{\"question\":\"How are Azure sentiment scores and social media posts used?\",\"answer\":\"The study extends the dataset with Azure sentiment scores and social media posts to test how sentiment information influences prediction accuracy, particularly for Tweets.\"}]","Predicting MBTI Personalities based on Language Usage by Natural Language Processing Algorithms | PDF"]