With the advent of the social web, users express their opinions and comments in social media. This user generated information has become a valuable asset to understand people opinions and can leverage the insights obtained from surveys with a targeted population.
Nevertheless, to understand the wisdom of the crowd, it is needed to develop technologies to collect, filter, analyse and visualise social media. Social media monitoring tools provide these facilities.
This final project aims at developing a social media monitoring system based on semantic technologies. The main functionalities of the system will be: (i) collecting social media using crawling and available APIs; (ii) ability to perform semantic analysis and expose these annotations as Linked Data; (iii) semantic query and filtering; (iv) visualisation and faceted search; and (v) scheduling of analysis.
For this purpose, the system will benefit from systems and services available in the GSI laboratory, such as GSI Crawler or Senpy Sentiment and Emotion Analysis services.
The main technologies used in the final work will be Elastic Search and Web Components. Elastic Search will be used for indexing and querying social media in a scalable way. Regarding Web Components, they will be used to enable the adaptation of the tool to different use cases.
The system will be evaluated through the development of several use cases, where performance and usability aspects will be evaluated.