Abstract
Personality refers to a person’s distinctive ways of thinking, feeling, and behaving in a variety of contexts. These traits are what consistently characterize a person’s behavior. It influences how we come to decisions, work through issues, resolves disputes, and handle stress. The Myers-Briggs type indicator is currently the most popular psychological type assessment in use worldwide. This dataset was obtained via Kaggle. It enables us to positively deal with people’s diversity by allowing us to anticipate certain personality traits in specific individuals. Many IT businesses are employing candidates with strong interpersonal communication abilities in today’s society. To measure these, we developed a method to determine their personality, which can help in decision-making. Using different machine learning approaches like SVM, DT, and LR, several past research has attempted to categorize people into different personality types. LSTM, a deep learning model, is now being used to help classify people’s personalities. In contrast to more conventional feedforward neural networks, LSTMs feature feedback connections. This property allows LSTMs to process entire data sequences without taking into account each point in the series individually, instead maintaining useful information about earlier data in the sequence to help in the processing of incoming data points to effectively classify the user texts. Finally, this supports the management, selection, and advancement of policies within organizations.
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References
Ahmad H, Asghar MZ, Khan AS, Habib A (2020) A systematic literature review of personality trait classification from textual content. Open Comput Sci 10(1):175–193
Osama M, El-Beltagy SR (2019) A transfer learning approach for emotion intensity prediction in microblog text. In: Proceedings of International conference on advance intelligent system informatics. Springer, Cham, Switzerland, pp 512–522
Asghar MZ, Sattar A, Khan A, Ali A, Masud Kundi F, Ahmad S (2019) Creating sentiment lexicon for sentiment analysis in Urdu: the case of a resource-poor language. Expert Syst 36(3):e12397
Khattak A, Habib A, Asghar MZ, Subhan F, Habib IAR (2021) Applying deep neural networks for user intention identification. Soft Comput 25(3):2191–2220
Ilimini K, Fernando TGI (2016) Persons’ personality traits recognition using machine learning algorithms and image processing techniques. Adv Comput Sci Int J 5(1):40–44
Asghar MZ, Khan A, Zahra SR, Ahmad S, Kundi FM (2019) Aspect-based opinion mining framework using heuristic patterns. Cluster Comput 22(S3):7181–7199
Subramanian K, Radhakrishnan VB, Ramasamy S (2014) Database independent human emotion recognition with meta-cognitive neuro-fuzzy inference system. In: Proceedings of 9th International conference on intelligent sensors network and information processing (ISSNIP), pp 1–6
Shivakumar G, Vijaya PA (2011) Facial expression based human emotion recognition with live computer response. Int J Comput Sci Inf Technol 4:81–84
Celli F (2012) Unsupervised personality recognition for social network sites. In: Proceedings of 6th International conference on digital society, pp 59–62
Lestari NMA, Putra IKGD, Cahyawan AAKA (2013) Personality types classification for Indonesian text in partners searching website using Naive Bayes methods. Int J Softw Inform Issues 173
Navya Sri M, Ramakrishna Murty M et al (2017) Robust features for emotion recognition from speech by using Gaussian mixture model classification. In: International conference and published proceeding in SIST series. Springer, vol 2, pp 437–444
Arifin, Purnaha KE (2012) Classification of emotions in Indonesia text using K-NN method. Int J Inf Electron Eng 2(6)
Firoj A, Speanov EA, Riccardi G (2013) Personality traits recognition on social network—Facebook. In: ICWSM workshop on personality classification
Iacobelli F, Cullota A (2013) Too neurotic, not too friendly: structured personality classification on textual data. In: ICWSM workshop on personality classification
Celli F, Pianesi F, Stillwell D, Kosinski M (2013) Workshop on computational personality recognition (shared task). In: Proceedings of WCPR13, in conjunction with ICWSM-13, p 201
MBTI Dataset from Kaggle. https://www.kaggle.com/datasets/datasnaek/mbti-type
Gjurkovi M, Karan M, Vukojevi I, Boánjak M, Snajder J (2021) PANDORA talks: personality and demographics on Reddit. In: Proceedings of 9th International workshop natural language processing and social media, pp 138–152. https://doi.org/10.18653/v1/2021.socialnlp-1.12
Chaudhary S, Sing R, Hasan ST, Kaur I (2018) A comparative study of different classifiers for Myers-Brigg personality prediction model. IRJET 5:1410–1413
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Raghavender, K.V., Ravi Kumar Raju, S., Alankruthi, S., Ashritha, M., Poojitha, G., Avyaktha, B. (2023). A Deep Learning Paradigm for Classifying Personality. In: Seetha, M., Peddoju, S.K., Pendyala, V., Chakravarthy, V.V.S.S.S. (eds) Intelligent Computing and Communication. ICICC 2022. Advances in Intelligent Systems and Computing, vol 1447. Springer, Singapore. https://doi.org/10.1007/978-981-99-1588-0_23
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DOI: https://doi.org/10.1007/978-981-99-1588-0_23
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