Feature selection based on Genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review

Kristiyanti, Dinar Ajeng; Wahyudi, Mochamad

doi:10.1109/citsm.2017.8089278

Cited by 26 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The author endorses using both types of comments in an equal ratio to achieve higher accuracy and efficiency. The authors of this work [14] proposed a framework in which the support vector machine method and three feature selection methods are used. The dataset comprises 200 reviews extracted from www.amazon.com.…”

Section: -Related Workmentioning

confidence: 99%

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

Jindal¹,

Aron²

2022

jist

View full text Add to dashboard Cite

Nowadays, social media platforms have become a mirror that imitates opinions and feelings about any specific product or event. These product reviews are capable of enhancing communication among entrepreneurs and their customers. These reviews need to be extracted and analyzed to predict the sentiment polarity, i.e., whether the review is positive or negative. This paper aims to predict the human sentiments expressed for beauty product reviews extracted from Amazon and improve the classification accuracy. The three phases instigated in our work are data pre-processing, feature extraction using the Bag-of-Words (BoW) method, and sentiment classification using Machine Learning (ML) techniques. A Global Optimization-based Neural Network (GONN) is proposed for the sentimental classification. Then an empirical study is conducted to analyze the performance of the proposed GONN and compare it with the other machine learning algorithms, such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). We dig further to cross-validate these techniques by ten folds to evaluate the most accurate classifier. These models have also been investigated on the Precision-Recall (PR) curve to assess and test the best technique. Experimental results demonstrate that the proposed method is the most appropriate method to predict the classification accuracy for our defined dataset. Specifically, we exhibit that our work is adept at training the textual sentiment classifiers better, thereby enhancing the accuracy of sentiment prediction.

show abstract

Section: -Related Workmentioning

confidence: 99%

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

Jindal¹,

Aron²

2022

jist

View full text Add to dashboard Cite

show abstract

“…The experimental outcomes displayed an improved accuracy with reduced training time. Kristiyanti et al [36] implemented three algorithms called principal component analysis (PCA), PSO and GA based feature reduction with SVM classifier. The accuracy of SVM classifier has been enhanced with these three algorithms.…”

Section: Optimized Feature Selection In Samentioning

confidence: 99%

A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

Daniel

Meena

2021

SCPE

View full text Add to dashboard Cite

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

show abstract

“…The experimental results showed an improved accuracy with reduced training time. Kristiyanti & Wahyudi, (2017) have implemented three algorithms called Principle Component Analysis (PCA), PSO and GA based feature reduction with SVM classifier. The accuracy of SVM classifier has been enhanced with these three algorithms.…”

Section: Optimized Feature Selection In Samentioning

confidence: 99%

A hybrid sentiment analysis approach using black widow optimization based feature selection

Daniel

Meena²

2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.

show abstract

Feature selection based on Genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review

Cited by 26 publications

References 12 publications

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

A hybrid sentiment analysis approach using black widow optimization based feature selection

Contact Info

Product

Resources

About