Sentiment analysis on large scale Amazon product reviews

Haque, Tanjim Ul; Saber, Nudrat Nawal; Shah, Faisal Muhammad

doi:10.1109/icird.2018.8376299

Cited by 184 publications

(80 citation statements)

References 1 publication

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“…However, it performed slightly worse compared to [20] when higher accuracy with Electronics dataset and Cell Phones and Accessories dataset. The introduced method was also slightly outperformed by linear SVM, while testing with Electronics dataset and Cell Phones and Accessories dataset.…”

Section: Resultsmentioning

confidence: 93%

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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: Resultsmentioning

confidence: 93%

“…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%

Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Korovkinas

Danėnas

Garšva

2020

NAMC

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“…Stop their worry regarding the usage of stop-words, 2. Identify words having higher volumes of search with a lower competition [14].…”

Section: B Feature Extraction Using Term Frequency-inverse Document Fmentioning

confidence: 99%

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

2019

IJITEE

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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.

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“…SVM achieved the better results as the standalone method to compare with NB. Rathor et al (2018), Haque et al (2018) showed that SVM can produce better results than other methods in sentiment analysis on Amazon product reviews. Liu and Lee (2018) reported SVM algorithm being the best option for Email sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

Korovkinas¹,

Danėnas²,

Garšva³

2019

BJMC

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The goal of this paper is to propose a hybrid technique to improve Support Vector Machines classification accuracy using training data sampling and hyperparameter tuning. The proposed technique applies clustering to select training data and parameter tuning to optimize classifier effectiveness. The paper reports that better results were obtained using our proposed method in all experiments, compared to results of method presented in our previous work.

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Sentiment analysis on large scale Amazon product reviews

Cited by 184 publications

References 1 publication

Support vector machine parameter tuning based on particle swarm optimization metaheuristic

Support vector machine parameter tuning based on particle swarm optimization metaheuristic

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

SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

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