Título da Dissertação
Tabela 5.2: Final results of the LDA topics aggregation. (*) topic labels different from Lansley and Longley lansley2016geography
['2* [BOLD] Topic Label', '[BOLD] Rio de Janeiro  [BOLD] No. Tweets', '[BOLD] Rio de Janeiro  [BOLD] Percentage (%)', '[BOLD] São Paulo  [BOLD] No. Tweets', '[BOLD] São Paulo  [BOLD] Percentage (%)', '2* [BOLD] Diff (%)']
-Ch3S5SS1SSSx2 LDA Model Resulting Topics The final model used in the implementation of our framework is defined to characterize a tweet into 50 different topics. Although that, in the experiment made to comprove the added-value brought by the model, we were obligated to cluster some of the topics due to the similarity presented in words constituting them. We first observe that the majority of topics is common in both cities, with exception of 4 topics: Weather and Shopping are not discussed in Rio de Janeiro; Antecipation & Socializing and Health are not discussed in São Paulo. We were not able to assign labels using the Lansley and Longley lansley2016geography taxonomy to 7 different topics. In such cases, we created our own labels, e.g., "BBB17"is about a highly popular reality TV show in Brazil named Big Brother Brazil.