Browsing by Subject "Preprocessing"

Now showing 1 - 3 of 3

Open Access
Personality traits prediction model from Turkish contents with semantic structures
(Springer, 2023-04-23) Kosan, Muhammed Ali; Karacan, Hacer; Ürgen, Burcu Ayşen
Users' personality traits can provide different clues about them in the Internet environment. Some areas where these clues can be used are law enforcement, advertising agencies, recruitment processes, and e-commerce applications. In this study, it is aimed to create a dataset and a prediction model for predicting the personality traits of Internet users who produce Turkish content. The main contribution of the study is the personality traits dataset composed of the Turkish Twitter content. In addition, the preprocessing, vectorization, and deep learning model comparisons made in the proposed prediction system will contribute to both current usages and future studies in the relevant literature. It has been observed that the success of the Bidirectional Encoder Representations from Transformers vectorization method and the Stemming preprocessing step on the Turkish personality traits dataset is high. In the previous studies, the effects of these processes on English datasets were reported to have lower success rates. In addition, the results show that the Bidirectional Long Short-Term Memory deep learning method has a better level of success than other methods both for the Turkish dataset and English datasets. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Open Access
Predicting personality traits with semantic structures and LSTM-based neural networks
(Elsevier, 2022-10) Kosan, Muhammed Ali; Karacan, Hacer; Ürgen, Burcu Ayşen
There is a need to obtain more information about target audiences in many areas such as law enforcement agencies, institutions, human resources, and advertising agencies. In this context, in addition to the information provided by individuals, their personal characteristics are also important. In particular, the predictability of personality traits of individuals is seen as a major parameter in making decisions about individuals. Textual and media data in social media, where people produce the most data, can provide clues about people's personal lives, characteristics, and personalities. Each social media environment may contain different assets and structures. Therefore, it is important to make a structural analysis according to the social media platform. There is also a need for a labelled dataset to develop a model that can predict personality traits from social media data. In this study, first, a personality dataset was created which was retrieved from Twitter and labelled with IBM Personality Insight. Then the unstructured data were transformed into meaningful and processable data, LSTM-based prediction models were created with the structural analysis, and evaluations were made on both our dataset and PAN-2015-EN. © 2022 THE AUTHORS
Open Access
Some heuristics as preprocessing for 0-1 integer programming
(1991) Yılmaz, Fatih
It is well-known that 0-1 integer programming is one of the hard problems to solve other than special cases of constraint set in mathematical programming. In this thesis, some preprocessing will be done to get useful informations, such as feasible solutions, bounds for the number of Ts in feasible solutions, about the problem. A new algorithm to solve general (nonlinear) 0-1 programming with linear objective function will be devoloped. Preprocessing informations, then, are appended to original problem to show improvements in enumerative algorithms, e.g. in Branch and Bound procedures.