In this study, sentiment analysis was developed and applied to technological products in the Twitter/X social network, also, the opinions expressed by customers were determined and finally the most suitable predictive model derived from Machine Learning was identified. For this purpose, 7102 tweets related to Apple and Samsung products were collected, using the methodology proposed by Erl, Khattak and Buhler which facilitated the implementation of its critical phases. The results obtained from sentiment analysis were evaluated using standard metrics such as Accuracy, Precision, Recall and F1-Score, applied to four machine learning models: K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF) and CatBoost Classifier (CC). Of these, the CatBoost Classifier proved to be the most effective, achieving 89% in Accuracy, 90% in Precision, 89% in Recall and 88% in F1-Score. It w...

Today X has become one of the most important socialnetworks for expressing opinions and interests on the web.The large amount of data generated allows automatedsystems to profile users based on gender, nationality andthematic interests. There are difficulties in this process notonly because of the short content, but also because of theambiguity and the use of several languages.The goal of this proposal is to generate a deep learningmodel using BERT that is able to identify demographic andthematic attributes from tweets. Pre-trained models of theBERT and Multilingual BERT type will be used, applied on PAN Author Profiling Task (CLEF 2019) corpora in English and Spanish.The proposed work will deepen the analysis using supervised classification data for gender and nationality classification and topic extraction through unsupervised techniques, such as LDA and BERTopic. These options include...