Bacharelado em Sistemas de Informação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12
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APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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11 resultados
Resultados da Pesquisa
Item Implementação de um sistema mobile colaborativo para acompanhamento do quadro de pacientes com esclerose múltipla por meio de análise de sentimento(2024-10-02) Araujo, Paula Priscila da Cruz; Gouveia, Roberta Macêdo Marques; Tschá, Elizabeth Regina; http://lattes.cnpq.br/9598413463162759; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/0280090820230057The study aims to develop a mobile system to facilitate the monitoring of patients with Multiple Sclerosis (MS), based on the Human-Centered Design (HCD) Toolkit to meet patient needs. The app allows patients to record and track emotions, symptoms, and treatments, offering monthly reports and personalized alerts. For sentiment analysis, the machine learning algorithms XGBoost and Naive Bayes were used, with XGBoost showing better performance, achieving 87.56% accuracy and an F1-Score of 0.876, while Naive Bayes obtained 62.25% accuracy and an F1-Score of 0.524. The results indicate the tool’s effectiveness in emotional and medical monitoring, contributing to an improved quality of life.Item Análise de sentimentos em reviews de jogos digitais da Plataforma Steam(2024-09-26) Albuquerque, Júlia de Melo; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584Sentiment analysis is an area that investigates the emotional expressions of human language, aiming to understand the underlying needs and opinions expressed in texts. Its complexity lies in the ability to discern not only the textual content but also the implicit emotional matrices. With technological advancements, the ease of publicly expressing opinions is disseminated through various means, with online gaming being a sector that attracts numerous player posts about various available titles. However, this diversity of audiences and topics makes it challenging to understand the expressed sentiment that pervades this universe. The aim of this study is to apply sentiment analysis techniques to digital game reviews, adopting an approach focused on supervised machine learning algorithms and pre-polarized libraries, in order to identify the best classification path capable of discerning the sentiments expressed by users in the reviews. This operation considers an approach with all opinions and another focused on each game’s specific genre. This analysis was conducted by exploring data from an online game distribution company (Steam), followed by data preparation due to the peculiarities present in the records. The results reveal that machine learning models outperform traditional approaches, such as using the VADER library, showing a higher precision by approximately 10% in captures. A difference of 20% more was observed in metrics such as recall and F1-score. This study represents an analytical contribution to the field of sentiment analysis, highlighting the model’s ability to deal with the complexity of human language.Item An implementation of a mathematical-computational method for the detection and treatment of financial outliers in higher education(2023-09-06) Freitas, Nathan Cavalcante; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/1613649528791400The Higher Education Census occurs annually, collecting data from public and private Higher Educational Institutions (HEI) in Brazil. Different factors can lead to anomalies or outliers in some of these collected data. This work proposes a mathematical-computational method to detect and treat atypical HEI’s financial values. Both univariate and bivariate analysis to that end. We analyzed the expenses and incomes of HEI in the census from 2016 to 2019. This analysis revealed that 204 out of 2,224 HEI, approximately 10%, reported some atypical data.Item Técnica de clusterização aplicada à análise de perfis socioeconômicos de estudantes concluintes de cursos de computação(2022-07-03) Souza, Clarissa Cordeiro de; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/1046530929912898The different social and economic classes of undergraduate students can impact the course of academic training and the permanence of such students in Brazilian higher education institutions. This course conclusion work applied a data mining technique called K-means clustering to the microdata of the 2017 National Student Performance Exam (ENADE), an exam applied by the National Institute of Educational Studies and Research Anísio Teixeira (Inep), with the aim of analyzing the contexts that separate graduates from the various computer courses, whether bachelor’s or licentiate, using socioeconomic data. The results pointed to four large groups of students and, based on their analysis, it is possible to list a profile of a graduate student of computing in the year analyzed, since the clusters have several characteristics in common, such as: most students are of the sex male, single, white, opted for the face-to-face modality, attended high school in public schools, among others. However, some characteristics were found in specific groups, for example there is a group of graduates who are from full-time public institutions.Item Análise de sentimentos em Tweets relacionados ao desmatamento da Floresta Amazônica(2021-12-17) Silva, Vinicius José Paes e; Andrade, Ermeson Carneiro de; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/7437953784606274The Amazon Forest is being devastated at the fastest pace in recent years. In 2021, the Amazon rainforest registers the largest accumulation of deforestation in 5 years, increasing from 13 thousand km2 between August 2020 and July 2021. An increase of 22% compared to the same period in the previous year, the highest number since 2006. Although many works address the issue of deforestation, none of them focus on analyzing the sentiments of the Brazilian population regarding the issue. This work presents an analysis of the sentiments of the Brazilian population related to the deforestation of the Amazon rainforest through the text mining of Twitter and aims to understand how Brazilian users opine and dialogue about the deforestation of the Amazon rainforest. The results reveal that Brazilian users tend to react to events related to deforestation in the Amazon forest on Twiter and that most users have a negative sentiment about the topic, reaching peaks of approximately 60% of tweets in a given time.Item Raspagem de Dados Jurídicos Utilizando Scrapy(2021-12-20) Barbosa, Jadiel Eudes Mendonça; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/8044959053132773Web scraping is a computational technique that uses a program to extract data that are hidden in web pages. In this way, this academic work aims to use how web scraping techniques to extract data from legal processes from the websites of the courts in order to help contracting companies to take strategic decisions with their legal departments.Item Técnicas de aprendizado de máquina para descoberta de conhecimento sobre dados abertos do ensino superior público brasileiro(2021-12-10) Rodrigues, Ebony Marques; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/5929185711837204This work deals with the use of techniques from the methods of Knowledge Discovery in Databases — KDD — and Cross Industry Standard Process for Data Mining — CRISPDM — on educational databases made available by the Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (National Institute of Educational Studies and Research Anísio Teixeira) — INEP — aiming to the discovery of knowledge that allows the identification, as well as the understanding, of the context in the formation of students from public Brazilian higher education institutions. Three data mining scenarios are observed, considering Supervised Machine Learning and Unsupervised Machine Learning methods, covering data classification, grouping and association experiments. The first scenario, which includes data from graduates of bachelor’s and licentiate’s undergraduate courses, aims to predict the approximate length of stay at graduation, considering the students’ socioeconomic information, through 16 classification models built using Decision Tree, Random Forest, XGBoost and Multilayer Perceptron Neural Network algorithms. XGBoost models had the best results in all experiments. In turn, the second scenario uses the Kmeans algorithm to perform a grouping of public higher education institutions that, based on the analysis of four groups obtained by considering information on expenses, numbers of professors and technicians, location and administrative category, among others, made it possible to identify similarities and dissimilarities between the institutions. The groups, in addition to data used in the first scenario, which include information about the students, such as age group, length of stay at graduation and form of admission to graduation, noting whether this occurred through affirmative action or social inclusion policies, among others, they are considered in the experiments of the third scenario, using the Apriori algorithm, for the generation of association rules that can support the discovery of knowledge in the context of Brazilian public higher education.Item Uma proposta para agrupamento automático de horas de trabalho(2022-06-03) Félix, Matheus Rodrigues de Souza; Monteiro, Cleviton Vinicius Fonsêca; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/6209976572558281The recording of hours used in projects is a recurring task in the day-to-day of most professionals. This task is crucial in processes related to administration and human resources for analysis of alignment with schedules and productivity. However, filling in correctly and on time are important points for the cycle of activities and registration to be effective. When the professional works on several projects in an alternating way in his daily life, the record of these activities tends to gain inaccuracy. In this article, a proposal will be presented to enable the automation of time recording through the use of text mining techniques. The objective of this project is to create a facilitator that helps the user by minimizing the daily hours creating records of work performed and increasing the accuracy of the records.Item Estudo comparativo de algoritmos de classificação supervisionada para classificação de polaridade em análise de sentimentos(2019) Albuquerque, Rotsen Diego Rodrigues de; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6441716676783585The huge increase of data on the Internet, it is a rich source for public opinion assessment of a specific subject. Consequently, the number of opinions available makes decision-making impossible if it is necessary to read and analyze all opinions. Since the use of Machine Learning has been widely used, I will present a comparative study of two algorithms for classifying movie comments using techniques of natural language processing and Sentiment Analysis. The data obtained were obtained manually where through the competition site called Kaggle where we have about 50,000 comments on various films. The purpose of this study is also to use the concepts of data science and Machine Learning, natural language processing and sentiment analysis to add more information about the entertainment and film industry. That is why these algorithms were created so that it is possible to show the results for this domain in the of movies comments registered in one big site/platform of movie industry, the famous IMDB. After training and testing, the machine had an accuracy of 86 % on predicting sentiments on commented text from movies.Item Aspect term extraction in aspect-based sentiment analysis(2019) Francisco, Alesson Delmiro; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340The increasing use of the Internet in many directions has created a necessity to analyze alarge quantity of data. A large amount of data is presented as Natural Language Text,which is unstructured, with many ways to express the same information. It is an importanttask to extract information and meaning from those unstructured content, such as opinionson products or services. The need to extract and analyze the large amount of data createdevery day on the Internet surpassed the capabilities of human ability, as a result, manytext mining applications that extract and analyze textual data produced by humans areavailable today, one of such kind of applications is Sentiment Analysis, viewed as a vitaltask both to the academic and commercial fields, so that companies and service providerscan use that knowledge extracted from textual documents to better understand how theircustomers think about them or to know how their products and services are appreciated ornot by their customers. However, the task of analysing unstructured text is a difficult one,that is why it is necessary to provide coherent information and concise summaries to thoserevisions. Sentiment Analysis is the process of computationally identifying and categorizingopinions expressed in a piece of text, especially in order to determine the writer’s attitudetowards a particular topic or product. Aspect-Based Sentiment Analysis is a sub-field ofSentiment Analysis that aims to extract more refined and exact opinions, by breakingdown text into aspects. Most of the current work in the literature does not take profitof either semantic-based resources or NLP-based analysis in the preprocessing stage. Tocountermeasure these limitations, a study on these resources is done aiming to extract thefeatures needed to execute the task, and to make the best combination for ATE. This workhas the main goal of implementing and analysing a method of Aspect Term Extraction(ATE) of users reviews (restaurants and laptops). The proposed method is based on asupervised approach called Conditional Random Fields (CRF) which is able to optimizethe use of features for classification, this choice was justified by previous related work thatdemonstrate the effectiveness of CRF for ATE. Also, we are investigating the existingmethods and features for ABSA, as well as proposing new features and experimentingwith feature combinations in order to find the best features combinations, that are not yetcovered in the state of art. The detailed study is done by experimenting with word features,n-grams and custom made features using an CRF supervised algorithm to accomplish thetask of Aspect Term Extraction with results in terms of Precision, Recall and F-measure,the standard evaluation metrics adopted in the field. Finally, a comparative assessmentbetween the proposal method for ATE against other related work presented in the literaturehas shown that the method presented by this work is competitive.