Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

doi:10.1063/1.5005351

Cited by 9 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, we propose a multi-view classification algorithm, called Multi-View K-Nearest Neighbors (MVKNN). We chose the KNN algorithm to apply multi-view learning because of its advantages such as simplicity, easy implementation, and effectiveness [39]. The advantages of KNN also include ease of understanding and interpretation of the results, and useful for nonlinear data.…”

Section: The Proposed Approach: Multi-view K-nearest Neighborsmentioning

confidence: 99%

An improved version of multi-view k-nearest neighbors (MVKNN) for multiple view learning

Kiyak¹,

Birant²,

Birant³

2021

Turkish Journal of Electrical Engineering and Computer Sciences

View full text Add to dashboard Cite

Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called Multi-View K-Nearest Neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved by the proposed MVKNN algorithm compared to the well-known machine learning algorithms (KNN, support vector machine, decision tree, and Naive Bayes) in the case of multi-view data. The results also show that our method outperforms the state-of-the-art multi-view learning methods in terms of accuracy.

show abstract

Section: The Proposed Approach: Multi-view K-nearest Neighborsmentioning

confidence: 99%

An improved version of multi-view k-nearest neighbors (MVKNN) for multiple view learning

Kiyak¹,

Birant²,

Birant³

2021

Turkish Journal of Electrical Engineering and Computer Sciences

View full text Add to dashboard Cite

show abstract

“…Rohaidah et al [9], introduced a new Sentiment Analysis approach using the k-NN classifier. The dataset was collected from customer review datasets with maximum precision results equal to 0.892.…”

Section: Related Work IImentioning

confidence: 99%

SMAD: Text Classification of Arabic Social Media Dataset for News Sources

Gaber¹,

El-din²,

Moussa³

2021

IJACSA

View full text Add to dashboard Cite

“…Ahmad et al, [12] had proposed yet another hybrid Ant Colony Optimization (ACO) along with the K-Nearest Neighbour (KNN) based algorithms like the feature selections used for choosing all relevant features from the datasets of customer review. The Information Gain (IG), the GA, and finally the Rough Set Attribute Reduction (RSAR) had been employed to be the baseline ones in a performance evaluation and comparison.…”

Section: Related Workmentioning

confidence: 99%

“…The actual distance from the separating hyper plane for a certain point on the H1 is 1/|W| and the Distance from a separating of the hyper plane to a particular point on the H2 is 1/|W| so that the maximum margin will be 2/|W|. An MMH will be rewritten to be a decision boundary as according to its Lagrangian formulation as in (12)…”

Section: E Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

Building Large Scale Cloud System for Product Sentiment Analysis using Hybrid Group Search Optimization Based Feature Selection

2019

IJITEE

View full text Add to dashboard Cite

A very powerful technology that performs complex computing in a massive scale is known as Cloud computing. There has been a massive growth that has been observed in the data scale which may also be big data which is generated by means of cloud computing which is observed. Sentiment Analysis, on the other hand, denotes the opinion extraction of users from the documents used for review. A sentiment classification that makes use of methods of Machine Learning (ML) can face problems in high dimensionality for a feature vector. Thus, the method of feature selection is needed for the elimination of all noisy and irrelevant features from a feature vector for efficiently working the ML algorithms. All chosen features will be sub-optimal owing to a Non-Deterministic Polynomial (NP) hard type of technique that was used. The Group Search Optimization (GSO) based algorithm which was on the basis of a method of feature selection will find some optimal feature subsets through the elimination of all redundant features. For this work, the method of feature selection based on the GSO was applied to the sentiment classification. There was also a method of feature selection which was hybrid and based on the GSO and Local Beam Search (LBS) that has beenproposed for a sentiment classification. The methods proposed were evaluated based on the product review dataset of Amazon. The results of the experiment proved that this method of a hybrid feature selection can outperform all other methods of feature selection for a sentiment classification.

show abstract

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

Cited by 9 publications

References 20 publications

An improved version of multi-view k-nearest neighbors (MVKNN) for multiple view learning

An improved version of multi-view k-nearest neighbors (MVKNN) for multiple view learning

SMAD: Text Classification of Arabic Social Media Dataset for News Sources

Building Large Scale Cloud System for Product Sentiment Analysis using Hybrid Group Search Optimization Based Feature Selection

Contact Info

Product

Resources

About