Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

Jose Angel Diaz-Garcia,Maria J Martin-Bautista,M Dolores Ruiz

doi:10.1109/access.2020.2990461

Jose Angel Diaz-Garcia, Maria J Martin-Bautista + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2990461

Copy DOI

Abstract

Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.

Highlights

Data Mining techniques, despite their recent novelty, are present in almost all research and development areas that human beings are currently working on
Known as Sentiment Analysis, Data Mining techniques are used to obtain relevant information from textual data coming from online social networks
We propose a new approach for sentiment analysis using generalized association rules, capable of summarizing a very huge set of tweets in a set of rules based on the 8 emotions characterized by Plutchik [9]

Summary

Introduction

Data Mining techniques, despite their recent novelty, are present in almost all research and development areas that human beings are currently working on. There are certain areas where these techniques stand out, remarkably influenced by the new economic and social tendencies where social networks have gained importance These areas are, for instance, the detection of communities [1], studies and tools focused on marketing [2], the development of predictive. Models in financial or insurance fields [3] and mining of social networks or sentiment analysis [4], [5] This last one has currently become one of the most studied aspects due to the growing interest in understanding users habits using more reliable analysis tools. In this field, known as Sentiment Analysis, Data Mining techniques are used to obtain relevant information from textual data coming from online social networks.

Objectives

Methods

Findings

Conclusion