Current work has carried out a particular sentiment analysis technique to capture the social pulse and the emerging trends, over microblogging data streams (produced by the twitter application). The purpose of this work is to get the twitter activity and understand people’s emotions, in relevance to specified thematic topics. The emotions studied overcome the typical positive-negative declaration to a wider emotional spectrum of six major emotions (recognized in the bibliography). For each emotion a scaling analysis is carried out towards a more fine-grained monitoring of emotions’ intensity.

Microblogging data streams analysis is a challenging field since it embeds difficulties in both the content published and the usage around it. Microblogging data are characterized by non-formal language writing, freestyle opinion expressing, and unpredictable and often unexpected post bursting. The state of the art is underpinned by mining techniques towards tweet topics and users recommendations and tweet hot topics detection. However, so far efforts suffer from several limitations—such as noise in tweets, lack of uniformity, and spam—thus not presenting an adequate solution to the problem of emotional patterns capturing. Therefore, automated emotion-wise mining methods are of particular importance to improve the wisdom of the crowds capturing. Because it’s common for users to post their opinions along with their feelings or attitude about them—we can view emotion classes and scales as natural units in such processed. It’s for this reason that automating the process of detecting and capturing emotions in tweet data sets can enhance understanding of trends and opinions evolution in such microblogging services.

Some of the potential applications that can be developed by CapturEmos are the following:

• Potential authorities of a place, although free web services are recommended to reach critical mass of users, mobile application could be sold as a barometer of people’s opinions on various investors.
• Companies interested in promoting their presence and products on the web and mobile application industry using sponsored web and a mobile application business models.
• Web services companies interested in integrated the results in their applications through API licensing or buyout options.
• Social services interested in assessing social media trends and human psychology in relation to technology emergence.