The Web is providing greater freedom for users to create and obtain information in a more dynamic and appropriate way. One means of obtaining information on this platform, which complements or replaces other forms, is the use of conversation robots or Chatterbots. Several factors must be taken into account for the effective use of this technology; the first of which is the need to employ a team of professionals from various fields to build the knowledge base of the system and be provided with a wide range of responses, i.e. interactions. It is a multidisciplinary task to ensure that the use of this system can be targeted to children. In this context, this chapter carries out a study of the technology of Chatterbots and shows some of the changes that have been implemented for the effective use of this technology for children. It also highlights the need for a shift away from traditional methods of interaction so that an affective computing model can be implemented.
Twitter has more than 313 million users, is considered a subjective social network of content generation on the Internet, not only used to disclose personal information, but shares opinions and information about events and events in general, being a great source of research for the discovery of knowledge among relevant data. In this context, the present work presents a study on the implementation (application) of the Recurrent Neural Network LSTM for the analysis and classification of tweets that are related to crime and not crime. The set of data that is the object of the survey comes from an account of a newspaper in the city of Belém, containing 20000 records in a period of two months. The results obtained with the use of RNR LSTM proved to be quite satisfactory, reaching accuracy in some cases of 90% correctness. Based on the results of this research, we observed the effectiveness of this method in the analysis of feelings in comparison with other algorithms such as Nayve Bayes, Convolutional Neural Networks used in the literature. Because it is a network that uses a Long-Term Memory technique, it can adapt to large time intervals, such as Twitter texts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.