İLGEN, BAHAR2019-10-162019-10-162019-10-24https://hdl.handle.net/11413/5419The usage of social media is increasing exponentially since it has been the easiest and fastest way to share information between people or organizations. As a result of this broad usage and activity of people on social networks, considerable amount of data is generated continuously. The availability of user generated data makes it possible to analyze personality of people. Personality is the most distinctive feature for an individual. The results of these analyses can be utilized in several ways. They provide support for human resources recruitment units to consider suitable candidates. Similar products and services can be offered to people who share the similar personality characteristics. Personality traits help in diagnosis of certain mental illnesses. It is also helpful in forensics to use personality traits on suspects to clarify the forensic case. With the rapid dissemination of online documents in many different languages, the classification of these documents has become an important requirement. Machine Learning (ML) and Natural Language Processing (NLP) methods are used to classify these digitized data. In this study, current ML techniques and methodologies have been used to classify text documents and analyze person characteristics from these datasets. As a result of classification, detailed information about the personality traits of the writer could be obtained. It was understood that the frequency-based analysis and the use of the emotional words at the word level are very important in the textual personality analysis.en-USAttribution-NonCommercial-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/Big Five Personality TraitsDeep Neural NetworkNatural Language ProcessingText MiningRNNLSTMconferenceObject2-s2.0-85075687011