Rodríguez Aguilar, RománRománRodríguez Aguilar2022-10-242022-10-24202197830308749409783030874957https://scripta.up.edu.mx/handle/20.500.12552/180010.1007/978-3-030-87495-7_6This work addresses the analysis of the content of the comments on Twitter in the period from December 2020 to February 2021 on the video streaming platforms in Mexico: Netflix, Disney+ and Prime Video. The analysis involves the extraction of comments on Twitter, cleaning the text and the development of a supervised support model for Text Mining for the sentiment classification of tweets in the categories: Positive, Negative or Neutral (spam); as well as the use of resampling techniques to measure the variability of the model’s performance and improve the precision of its parameters. The result allows the measurement of user satisfaction levels and the detection of the most dissatisfied and liked aspects of the platforms. Finally, a business intelligence dashboard was developed in Power BI for the interactive visualization of the results under different information filters. The results show that there is a large percentage of Neutral tweets (spam) that refer mainly to advertising about new releases. Netflix’s satisfaction level is the highest compared to the rest of the platforms due to the liking for its original series, variety, and dynamism of launches; on the contrary, the most unpleasant aspect is removing content from your catalog. For its part, Disney+ has satisfaction lower due to the limited variety of its catalog and the expense involved. In the case of Prime Video, lower levels of satisfaction are observed for removing content from its catalog and for paying more than one platform per month. The application of this methodology could benefit in measurement of satisfaction levels, understanding, decision-making and monitoring of new strategies implemented by the platforms.enText miningSentiment analysisAnalytical intelligenceStreamingSatisfaction levelsResource Types::text::conference output::conference proceedings::conference paper