Learning from students' perception on professors through opinion mining

Students' perception of classes measured through their opinions on teaching surveys allows to identify deficiencies and problems, both in the environment and in the learning methodologies. The purpose of this paper is to study, through sentiment analysis using natural language processing (NLP) and machine learning (ML) techniques, those opinions in order to identify topics that are relevant for students, as well as predicting the associated sentiment via polarity analysis. As a result, it is implemented, trained and tested two algorithms to predict the associated sentiment as well as the relevant topics of such opinions. The combination of both approaches then becomes useful to identify specific properties of the students' opinions associated with each sentiment label (positive, negative or neutral opinions) and topic. Furthermore, we explore the possibility that students' perception surveys are carried out without closed questions, relying on the information that students can provide through open questions where they express their opinions about their classes

A performance comparison of oversampling methods for data generation in imbalanced learning tasks

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMClass Imbalance problem is one of the most fundamental challenges faced by the machine learning community. The imbalance refers to number of instances in the class of interest being relatively low, as compared to the rest of the data. Sampling is a common technique for dealing with this problem. A number of over - sampling approaches have been applied in an attempt to balance the classes. This study provides an overview of the issue of class imbalance and attempts to examine some common oversampling approaches for dealing with this problem. In order to illustrate the differences, an experiment is conducted using multiple simulated data sets for comparing the performance of these oversampling methods on different classifiers based on various evaluation criteria. In addition, the effect of different parameters, such as number of features and imbalance ratio, on the classifier performance is also evaluated

Analyzing digital societal interactions and sentiment classification in Twitter (X) during critical events in Chile

This study explores the influence of social media content on societal attitudes and actions during critical events, with a special focus on occurrences in Chile, such as the COVID-19 pandemic, the 2019 protests, and the wildfires in 2017 and 2023. By leveraging a novel tweet dataset, this study introduces new metrics for assessing sentiment, inclusivity, engagement, and impact, thereby providing a comprehensive framework for analyzing social media dynamics. The methodology employed enhances sentiment classification through the use of a Deep Random Vector Functional Link (D-RVFL) neural network, which demonstrates superior performance over traditional models such as Support Vector Machines (SVM), naive Bayes, and back propagation (BP) neural networks, achieving an overall average accuracy of 78.30% (0.17). This advancement is attributed to deep learning techniques with direct input–output connections that facilitate faster and more precise sentiment classification. This analysis differentiates the roles of influencers, press radio, and television handlers during crises, revealing how various social media actors affect information dissemination and audience engagement. By dissecting online behaviors and classifying sentiments using the RVFL network, this study sheds light on the effects of the digital landscape on societal attitudes and actions during emergencies. These findings underscore the importance of understanding the nuances of social media engagement to develop more effective crisis communication strategies

A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis

International audienceThe majority of Twitter sentiment analysis systems implicitly assume that the class distribution is balanced while in practice it is usually skewed. We argue that Twitter opinion mining using learning methods should be addressed in the framework of imbalanced learning. In this work, we present a study of synthetic oversampling techniques for tweet-polarity classification. The experiments we conducted on three publicly available datasets show that these methods can improve the recognition of the minority class as well as the geometric mean criterion