CLASSIFICATION OF TWEETS WITH A MIXED METHOD BASED ON PRAGMATIC CONTENT AND META-INFORMATION
Free (open access)
Volume 13 (2018), Issue 1
60 - 70
M. ESTEVE, F. MIRÓ & A. RABASA
The sharp rise in social networks in any field of opinion has led to the increasing importance of content analysis. Due to the concretion of the texts published on Twitter from its limitation to 140 characters, this network is the most suitable for the analysis and classification of opinions according to different criteria. Therefore, there are multiple tweet analysis tools oriented from the perspective of semantics for trying to classify content characteristics such as feeling and polarity.
In this paper, the authors present a new approach to classification from a different perspective. The proposed approach addresses a complex mixed model from a perspective of pragmatics, the analysis of opinions in the context of their issuer carried out by a panel of experts, along with the classification of the type of discourse by considering the meta-information of the tweet.
From this new approach, the paper presents a complete and complex analysis process of Big Data, which covers all the characteristic phases of the life cycle: capture, storage, preprocessing and analysis of a tweets database. The aim is to classify the tweets as violent or non-violent in their reference to terrorist acts.
If the classification models based on the metadata of tweets reach acceptable levels of accuracy, this methodology will offer a reliable and semiautomatic alternative for tweet classification.
analysis, big data, classification, social networks