TECLA: A temperament and psychological type prediction framework from Twitter data.

Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user's social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey's model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair.

Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis.

Bladder cancer occurs in the epithelial lining of the urinary bladder and is amongst the most common types of cancer in humans, killing thousands of people a year. This paper is based on the hypothesis that the use of clinical and histopathological data together with information about the concentration of various molecular markers in patients is useful for the prediction of outcomes and the design of treatments of nonmuscle invasive bladder carcinoma (NMIBC). A population of 45 patients with a new diagnosis of NMIBC was selected. Patients with benign prostatic hyperplasia (BPH), muscle invasive bladder carcinoma (MIBC), carcinoma in situ (CIS), and NMIBC recurrent tumors were not included due to their different clinical behavior. Clinical history was obtained by means of anamnesis and physical examination, and preoperative imaging and urine cytology were carried out for all patients. Then, patients underwent conventional transurethral resection (TURBT) and some proteomic analyses quantified the biomarkers (p53, neu, and EGFR). A postoperative follow-up was performed to detect relapse and progression. Clusterings were performed to find groups with clinical, molecular markers, histopathological prognostic factors, and statistics about recurrence, progression, and overall survival of patients with NMIBC. Four groups were found according to tumor sizes, risk of relapse or progression, and biological behavior. Outlier patients were also detected and categorized according to their clinical characters and biological behavior.

A multi-label, semi-supervised classification approach applied to personality prediction in social media.

Social media allow web users to create and share content pertaining to different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data scientists seeking to understand behaviours and trends, whilst collecting statistics for social sites. One potential application for these data is personality prediction, which aims to understand a user's behaviour within social media. Traditional personality prediction relies on users' profiles, their status updates, the messages they post, etc. Here, a personality prediction system for social media data is introduced that differs from most approaches in the literature, in that it works with groups of texts, instead of single texts, and does not take users' profiles into account. Also, the proposed approach extracts meta-attributes from texts and does not work directly with the content of the messages. The set of possible personality traits is taken from the Big Five model and allows the problem to be characterised as a multi-label classification task. The problem is then transformed into a set of five binary classification problems and solved by means of a semi-supervised learning approach, due to the difficulty in annotating the massive amounts of data generated in social media. In our implementation, the proposed system was trained with three well-known machine-learning algorithms, namely a Naïve Bayes classifier, a Support Vector Machine, and a Multilayer Perceptron neural network. The system was applied to predict the personality of Tweets taken from three datasets available in the literature, and resulted in an approximately 83% accurate prediction, with some of the personality traits presenting better individual classification rates than others.