GAWA–A Feature Selection Method for Hybrid Sentiment Classification

Abstract

Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set upto 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

“…In particular, an emotion‐improved LSTM, termed ELSTM, becomes the first one modeled by using EI for improving the feature learning capability of the LSTM network, which achieves emotion variation of the learning mechanism through the presented emotion estimator and emotion modulator. Rasool et al 14 devised a method called GAWA for selecting features through the wrapper approaches (WA) for selecting the genetic algorithm (GA) and the premier aspects for minimizing the magnitude of the premier features. The study proposes the altered fitness function of heuristic GA for computing the optimum aspects through the reduction of duplication for superior precision.…”

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

“…There exist studies that combine multiple feature selection methods to enhance the efficiency of the sentiment analysis task. Rasool et al [17] proposed a hybrid feature selection method for sentiment classification. They selected promising features using different wrapper approaches and transferred them to the population of their Genetic Algorithm.…”

“…majority voting). Although lexicon-based methods are easy to apply, they suffer from the lack of domain-specific dictionaries [17]. While machine learning techniques have achieved promising VOLUME 9, 2021 improvements over lexicon-based approaches, they require feature engineering for natural language processing (NLP) tasks [18].…”

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

“…Finally, in classification, the classifier is trained using the dataset's selected features to distinguish between depressive and non‐depressive users. The FS technique in DL aims to improve classification AC while reducing dimensionality and computational complexity (Rasool et al, 2020). The DL approaches traditionally uses two kinds of FS mechanisms: filter‐based and wrapper‐based.…”