Feature Selection Methods in Sentiment Analysis and Sentiment Classification of Amazon Product Reviews

Shaikh, Tahura; Deshpande, Deepa S.

doi:10.14445/22312803/ijctt-v36p139

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…The main advantage of the introduced method is that it can be easily applied on any linear SVM instance for textual data classification tasks on a large datasets and perform faster than ordinary linear SVM when it is used in combination with our method presented in [27]. In this paper, we found that using only 70,000 instances for training instead of more than 20 million (Books dataset) to develop classifier still resulted in performance comparable to [37,40,44], and the results obtained are also competitive with state-of-theart models.…”

Section: Discussionmentioning

confidence: 90%

“…Dataset splits into training and testing data are different and demonstrate that sufficient accuracy can be obtained using a smaller training subset. Table 4 shows that LSVM PSO and its ensemble of three CL3_LSVM PSO resulted in higher accuracy compared to [37] and [40] when applied on the largest Books dataset and Kindle Store dataset [37]. The proposed method and its ensembles also outperform CNN-S(+), CFM, and PFM when they were applied on Cell Phones and Accessories dataset to compare with [44].…”

Section: Resultsmentioning

confidence: 99%

“…Next, four experiments were done to compare the results with other authors' works. The datasets (Books, Electronics, Kindle Store, Cell Phones, and Accessories) used in [20,37,40] were selected. The descriptions of these datasets are presented in Table 1 (see Section 4.1).…”

Section: Methodsmentioning

confidence: 99%

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Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Korovkinas

Danėnas

Garšva

2020

NAMC

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This paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority voting based ensembling is applied to increase the efficiency of the proposed method. The results were compared with results from our previous research and other authors' works. They indicate that the proposed method can improve classification performance for a sentiment recognition task.

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Section: Discussionmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

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Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Korovkinas

Danėnas

Garšva

2020

NAMC

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“…In [1], three types of feature extraction classifiers were used. 1) Phrase level, where phrases/sentences were used as features.…”

Section: Literature Surveymentioning

confidence: 99%

Opinion Mining Classification u sing Naive Bayes Algorithm

Vangara*¹,

Thirupathur²,

Vangara³

2020

IJITEE

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With the recent advancement in the field of online services, the importance of a review for a product has also gone up. In this paper we focus on the aspect of reducing the time and effort for the user by recommending the best product to him. For this to be achieved, this paper proposes a Naive Bayes Classifier which labels the reviews accurately and combines the reviews to give a final rating to the product. The amazon product review data consisting of both negative and positive reviews was used for training and testing purposes. The model’s performance is evaluated, and results are analysed.

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“…Elli Maria, Yi-Fan [10] in "Amazon Reviews, Business Analytics with Sentiment Analysis" extracted sentiment from the reviews and analyze the result to build up a business model. The aim of this paper is to extract sentiment from more than 2.7 million reviews and analyze the implications they have in the business area.…”

Section: This Research Work By Hatzivassiloglouand Mckeown [8]mentioning

confidence: 99%

Sentiment Analysis on Large Scale Amazon Product Reviews

Johar,

Mubeen

2020

IJSRCSE

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The world nowadays is becoming more digitalized. In this digitalized world e-commerce is taking the ascendancy by making products available within the reach of customers where the customer doesn't have to go out of their house. As now a day's people are relying on online products so the importance of a review is going higher. For selectng a product, a customer needs to go through thousands of reviews to understand a product. But in this prospering day of machine learning, going through thousands of reviews would be much easier if a model is used to polarize those reviews and learn from it. So by implementing supervised learning method on a large scale Amazon dataset to polarize it and get satisfactory accuracy.

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Feature Selection Methods in Sentiment Analysis and Sentiment Classification of Amazon Product Reviews

Cited by 12 publications

References 2 publications

Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Opinion Mining Classification u sing Naive Bayes Algorithm

Sentiment Analysis on Large Scale Amazon Product Reviews

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