Building a fuzzy sentiment dimension for multidimensional analysis in social networks

Abstract

The continuous growth of social networks has made them one of the main information sources for researchers and companies, but at the same time, their pre-processing and analysis represent a great challenge. In this work, we create a Fuzzy Sentiment Dimension from textual data from social networks to allow sentiment analysis jointly with standard dimensions in a multidimensional model, and make easier and more flexible sentiment analysis in social networks. In particular, we use technologies such as fuzzy logic and multidimensional analysis, together with unsupervised tools for sentiment analysis. The use of unsupervised tools for sentiment analysis also allows both previously labeled and unlabeled documents to be analyzed. The performance results of the experimentation demonstrate the feasibility of the proposal.