Paper Info
Title
MISNIS: An intelligent platform for twitter topic mining.
Abstract
Abstract Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a useru0027s will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweetu0027s corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations.
Year
Venue
Field
2017
Expert Syst. Appl.
Topic mining,Categorization,Data mining,World Wide Web,Social network,Computer science,Sentiment analysis,Expert system,Portuguese,Search engine indexing
DocType
Volume
Citations 
Journal
89
5
PageRank 
References 
Authors
0.45
36
4
Name
Order
Citations
PageRank
João Paulo Carvalho111017.52
Hugo Rosa2201.82
Gaspar Brogueira3162.37
Fernando Batista411521.04