TCC - Bacharelado em Sistemas da Informação (UAEADTec)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/2881
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2 resultados
Resultados da Pesquisa
Item Análise de sentimentos em publicações do Stackoverflow(2019-08-22) Santos, Luiz Felipe dos; Trindade, Cleyton Carvalho da; http://lattes.cnpq.br/6298429503812388The use of social networks, forums and various media has been growing exponentially, reflecting directly on the amount of data generated on the Internet, a large portion of the data generated, are open and can be accessed and processed. As a result, the possibilities generated by open data have attracted many researchers and companies to extract valuable information about their customers. Information extracted from this mass of data can change the strategy of many companies and people. In computer forums, you can see the same pattern, multiple people interacting and generating various information about information technology and its derivatives. The research will go through the whole cycle of sentiment analysis, data capture on the StackOverflow platform, data processing,natural language processing, algorithm training and classification. In order to show the data processing and classification steps, compare the classification approaches and extract information about the analyzed database. After applying the sentiment analysis cycle, it was possible to compare the results of each classifier and extract information about the analyzed database, about the performance of the unstructured classifiers and the difficulty of working with the language Portuguese database.Item Análise do comportamento através dos dados coletados na internet(2021-04-07) Lima, Priscilla Amarante de; Diniz, Juliana Regueira Basto; http://lattes.cnpq.br/0175193064988810; http://lattes.cnpq.br/7284770857817456This work presents an analysis of human behavior through data collected on the internet. They will be confirmed as Big Techs and the Cambridge Analytica case study. We show that digital records of behavior easily obtained, through likes, through Facebook can be used to automatically and accurately predict a range of highly confidential personal attributes, including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, separation from parents, age and sex. The based analysis is based on a data set of more than 58,000 volunteers who provided Facebook likes, detailed demographic profiles and the results of various psychometric tests. The proposed model uses dimensionality reduction to pre-process the tanned data, which is then inserted in linear regression to predict individual psych demographic profiles of tanned. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African-Americans and Caucasian Americans in 95% of cases, and between Democrats and Republicans in 85% of cases. For the personality trait "Aperture", prediction accuracy is close to the test-retest accuracy of a personality test pattern. We give examples of associations between attributes and likes and discuss it as a conclusion for online personalization and privacy.