Big Data and Automatic Detection of Topics: Social Network Texts

Amelec (Jesus) Viloria (Silva) ,Noel Varela,William Niebles Núñez,Hugo Hernández Palma,Alex Ruiz-Lazaro

doi:10.1088/1742-6596/1432/1/012073

Abstract

This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.

Highlights

Today, the social networks is recognized and, as a consequence, the increase in the number of users interacting in these networks, which causes the accumulation of large volumes of unstructured textual data
This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks
This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics

Summary

Introduction

The social networks is recognized and, as a consequence, the increase in the number of users interacting in these networks, which causes the accumulation of large volumes of unstructured textual data. Motivated by the previous problem, this paper analyzes the influence of the terms that express feelings in the automatic detection of topics in social networks This paper analyses the influence of sentimentally oriented terms in the detection of topics in social networks For this purpose, a filter is applied during semantic pre-processing with the aim of eliminating the terms that express feelings, which can introduce noise in the detection of topics. This proposal is totally new since, unlike the mentioned works where the detection of topics is merged with the analysis of feelings, in this case what is done is to discard the terms related to feelings, remaining only the terms that provide useful information for the automatic detection of the main topics

Methods

Results & Discussions

Conclusions