A Smart System for Twitter Corpus Collection, Management and Visualization

Social networks have become popular and are now becoming an alternate mean of communication, used to share information on various topics, ranging from politics or sports to simple aspects of everyday life. Twitter messages (tweets) are shared in real time and are essentially public, making them a useful source of information for areas such as tourism, marketing, health, and safety. This paper describes an information system that involves the creation and storage of a corpus of tweets, written in European Portuguese and published within the Portuguese territory. The system also involves a REST API that allows access to the stored information, and a web-based dashboard that makes it possible to analyze and visualize indicators concerning the stored data.

[1]  Abdolreza Abhari,et al.  Cluster-discovery of Twitter messages for event detection and trending , 2015, J. Comput. Sci..

[2]  Matthew S. Gerber,et al.  Predicting crime using Twitter and kernel density estimation , 2014, Decis. Support Syst..

[3]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[4]  Mourad Oussalah,et al.  A software architecture for Twitter collection, search and geolocation services , 2013, Knowl. Based Syst..

[5]  Roberto V. Zicari,et al.  PoliTwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis , 2014, Knowl. Based Syst..

[6]  J. Wishart Encyclopedia of Mobile Phone Behavior , 2015 .

[7]  Ricardo Ribeiro,et al.  Sentiment Analysis and Topic Classification based on Binary Maximum Entropy Classifiers , 2013, Proces. del Leng. Natural.

[8]  David Zimbra,et al.  Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network , 2013, Expert Syst. Appl..

[9]  Sérgio Matos,et al.  Analysing Twitter and web queries for flu trend prediction , 2014, Theoretical Biology and Medical Modelling.

[10]  Daniel A. Gruber,et al.  The real-time power of Twitter: Crisis management and leadership in an age of social media , 2015 .

[11]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[12]  Patric R. Spence,et al.  Expressions of risk awareness and concern through Twitter: On the utility of using the medium as an indication of audience needs , 2014, Comput. Hum. Behav..

[13]  Yücel Saygin,et al.  Sentimental causal rule discovery from Twitter , 2014, Expert Syst. Appl..

[14]  Wenwen Li,et al.  Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US , 2014 .

[15]  E. Larson,et al.  Dissemination of health information through social networks: twitter and antibiotics. , 2010, American journal of infection control.

[16]  Fernando Batista,et al.  MISNIS: An intelligent platform for twitter topic mining , 2017, Expert Syst. Appl..

[17]  Marie-Luce Bourguet,et al.  An Overview of Multimodal Interaction Techniques and Applications , 2009 .

[18]  Víctor M. Prieto,et al.  Twitter: A Good Place to Detect Health Conditions , 2014, PloS one.