Ant colony optimization for text feature selection in sentiment analysis

Ahmad, Siti Rohaidah; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

doi:10.3233/ida-173740

Cited by 38 publications

(15 citation statements)

References 34 publications

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“…The Wrapper feature selection approach has been widely used in numerous applications, e.g., in the medical field for the calculation of optimum features from coronary artery disease [43]. The author [44] presented a wrapper approach for sentiment polarity classification by the integration of genetic algorithm and SVM classifier. It concluded that the accuracy of polarity classification had been improved by attaining the optimal features sets from the Internet Movie Database (IMDb).…”

Section: Related Workmentioning

confidence: 99%

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

et al. 2020

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“…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. Kumar et al, [22] proposed binary cuckoo search for feature selection and employed the TF-IDF weighting schemes and SVM classifier to utilize the optimal features for enhancing the sentiment analysis accuracy.…”

Section: Related Workmentioning

confidence: 99%

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Anuprathibha*¹,

Selvi²

2019

IJRTE

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Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers

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“…Researchers in [26] have hybridised ant colony optimisation (ACO) and K-nearest neighbour (KNN) as a feature selection technique to produce an optimum feature set that can help to yield high classification and clustering accuracy. They used electrical product data from Nokia, Canon, Apex, Creative, and Nikon.…”

Section: A Survey On Feature Selection Using Swarm Algorithm In Sementioning

confidence: 99%

A Review of Feature Selection and Sentiment Analysis Technique in Issues of Propaganda

Ahmad¹,

Zakwan²,

Syafira³

et al. 2019

IJACSA

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Propaganda is a form of communication that is used in influencing communities, or people in general, to push forward an agenda for a certain goal. Nowadays, there are different means used in distributing propaganda including postings on social media, illustrations, cartoons and animations, articles, TV and radio shows. This paper is focused on election propaganda. Candidates in elections would use propaganda as a form of communication to channel and deliver messages through social media. Sentiment analysis (SA) is then used in identifying the positive and negative elements within the propaganda itself, through analysing the related documents, social media, articles or forums. This paper presents the various techniques used by previous researchers in issues of propaganda using SA, which include feature selection to remove irrelevant features and sentiment methods to identify sentiment in documents or others. Feature selection is a dominant side in sentiment analysis due to content of textual has a high measurement classification that can jeopardize SA classification interpretation. This paper also explores several SA techniques to identify sentiments in issues of propaganda. This study has also attempted to identify the use of swarm algorithms as a suitable feature selection method in SA for propaganda issues.

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Ant colony optimization for text feature selection in sentiment analysis

Cited by 38 publications

References 34 publications

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

A Review of Feature Selection and Sentiment Analysis Technique in Issues of Propaganda

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