The use of new information and communication technologies (ICTs) has promoted changes in the way in which information is distributed. A challenge is to explore the integration of new ICTs, such as smartphones and chatbots, to disseminate information within a regional museum. In this paper, we proposed the development of a chatbot to the Regional Museum of São João del-Rei to present his most outstanding artifact, a painting named Retrato de Menina. This paper presents the chatbot design in which was implemented on the IBM Watson platform. An evaluation with users was performed applying the Wizard of Oz method. The results of the evaluation evidenced that the proposed chatbot satisfies users, although it is necessary to improve the communication flow in order to guide users in the chatbot's interaction. The results showed that implementing several chatbots (i.e., one for each artifact of the museum) is better than making a single one, because this way avoids failures in determining dialog contexts and explore more playful interaction styles, such as chatbots that pretend to be the character of some artifact.

In this paper, we propose a general framework that allows developers to filter, summarize and analyze user reviews written about applications on App Stores. Our framework extracts automatically relevant features from reviews of apps (e.g., information about functionalities, bugs, requirements, etc) and analyzes the sentiment associated with each of them. Our framework has three main building blocks, namely, (i) topic modeling, (ii) sentiment analysis and (iii) summarization interface. The topic modeling block aims at finding semantic topics from textual comments, extracting the target features based on the most relevant words of each discovered topic. The sentiment analysis block detects the sentiment associated with each discovered feature. The summarization interface provides to developers an intuitive visualization of the features (i.e., topics) and their associated sentiment, providing richer information than a 'star rating' strategy. Our evaluation shows that the topic modeling block is able to organize information provided by users in subcategories that facilitate the understanding of which features more positively/negatively impact the overall evaluation of the application. Regarding user satisfaction, we can observe that, in spite of the star rating being a good measure of evaluation, the Sentiment Analysis technique is more accurate in capturing the sentiment transmitted by the user by means of a comment.

Social media applications have assumed an important role in decision-making process of users, affecting their choices about products and services. In this context, understanding and modeling opinions, as well as opinion-leaders, have implications for several tasks, such as recommendation, advertising, brand evaluation etc. Despite the intrinsic relation between opinions and opinion-leaders, most recent works focus exclusively on either understanding the opinions, by Sentiment Analysis (SA) proposals, or identifying opinion-leaders using Influential Users Detection (IUD). This paper presents a preliminary evaluation about a combined analysis of SA and IUD. In this sense, we propose a methodology to quantify factors in real domains that may affect such analysis, as well as the potential benefits of combining SA Methods with IUD ones. Empirical assessments on a sample of tweets about the Brazilian president reveal that the collective opinion and the set of top opinion-leaders over time are inter-related. Further, we were able to identify distinct characteristics of opinion propagation, and that the collective opinion may be accurately estimated by using a few top-k opinion-leaders. These results point out the combined analysis of SA and IUD as a promising research direction to be further exploited.