A Hybrid Hierarchical Mathematical Heuristic Solution of Sparse Algebraic Equations in Sentiment Analysis

Author:

Jalali Maryam1,Zahedi Morteza1ORCID,Gharahbagh Abdorreza Alavi2ORCID,Hajihashemi Vahid2ORCID,Machado José J. M.3ORCID,Tavares João Manuel R. S.3ORCID

Affiliation:

1. Faculty of Computer and IT Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran

2. Faculdade de Engenharia, Universidade do Porto (FEUP), Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal

3. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal

Abstract

Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation systems that can be used as a sentiment word scoring system. In its first step, the proposed solution uses two mathematical approaches to find two initial populations for a heuristic method. The heuristic solution solves a pseudo-linear NLP scoring scheme in a polarity detection method and determines the final scores. The proposed solution was validated using three scenarios on the SemEval-2013 competition, the ESWC dataset, and the Taboada dataset. The simulation results revealed that the proposed solution is comparable to the best state-of-the-art methods in polarity detection.

Publisher

MDPI AG

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