Discovering conversational topics and emotions associated with Demonetization tweets in India

Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter's data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA) based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people's opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by other author

Emotional Reactions to the Perception of Risk in the Pompeii Archaeological Park

The assessment of perceived risk by people is extremely important for safety and security management. Each person is based on the opinion of others to make a choice and the Internet represents the place where these opinions are mostly researched, found and reviewed. Social networks have a decisive impact: 92% of consumers say they have more trust in social media reviews than in any other form of advertising. For this reason, Opinion Mining and Sentiment Analysis have found interesting applications in the most diverse context, among which the most innovative is certainly represented by public safety and security. Security managers can use the perceptions expressed by people to discover the unexpected and potential weaknesses of a controlled environment or otherwise the risk and security perception of people that sometimes can be very different from real level of risk and security of a given site. Since the perceptions are the result of mostly unconscious elaborations, it is necessary to go deeper and to search for the emotions, triggered by the sensorial stimuli, that determine them. The objective of this paper is to study the perception of risk within the Pompeii Archaeological Park, giving emphasis to the emotional components, using the semantic analysis of the textual contents present in Twitter.Peer reviewe