A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction

Abstract

Due to the rapid development of Internet technologies and social media, sentiment analysis has become an important opinion mining technique. Recent research work has described the effectiveness of different sentiment classification techniques ranging from simple rule-based and lexicon-based approaches to more complex machine learning algorithms. While lexicon-based approaches have suffered from the lack of dictionaries and labeled data, machine learning approaches have fallen short in terms of accuracy. This paper proposes an integrated framework which bridges the gap between lexicon-based and machine learning approaches to achieve better accuracy and scalability. To solve the scalability issue that arises as the feature-set grows, a novel genetic algorithm (GA)-based feature reduction technique is proposed. By using this hybrid approach, we are able to reduce the feature-set size by up to 42% without compromising the accuracy. The comparison of our feature reduction technique with more widely used principal component analysis (PCA) and latent semantic analysis (LSA) based feature reduction techniques have shown up to 15.4% increased accuracy over PCA and up to 40.2% increased accuracy over LSA. Furthermore, we also evaluate our sentiment analysis framework on other metrics including precision, recall, F-measure, and feature size. In order to demonstrate the efficacy of GA-based designs, we also propose a novel cross-disciplinary area of geopolitics as a case study application for our sentiment analysis framework. The experiment results have shown to accurately measure public sentiments and views regarding various topics such as terrorism, global conflicts, and social issues. We envisage the applicability of our proposed work in various areas including security and surveillance, law-and-order, and public administration.

Framework for opinion as a service on review data of customer using semantics based analytics

“…The work of Mullick et al [20] presents a technique to extract particular feature from the social network using learning-based technique. Usage of genetic algorithm was seen in the work of Iqbal et al [21] where the focus was over reduction of the feature. A discrete model was constructed for computing social opinion by Wu et al [22] where voting-based approach was used for performing predictive mining analysis.…”

Sentiment Analysis Using Hybrid Feature Selection Techniques

“…In Iqbal et al [22] researchers proposed a hybrid framework to solve scalability problems that appear when feature set grows in sentiment analysis. Using genetic algorithm (GA) based technique to reduce feature set size up to 42% without effecting accuracy.…”

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

“…This approach utilized information gain for feature reduction and SVM for classification with high accuracy and less execution time. Iqbal et al, [20] developed a hybrid sentiment analysis framework using genetic algorithm and increased the accuracy. Ahmad et al, [21] proposed ant colony optimization (ACO) for feature selection using a wrapper approach with integrated ACO for feature selection and KNN for classification.…”