Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

Sadhasivam, Jayakumar; Kalivaradhan, Ramesh Babu

doi:10.33889/ijmems.2019.4.2-041

Cited by 22 publications

(6 citation statements)

References 6 publications

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“…In case the trial matches something the model has observed afore time, it can custom the ‘prior learning’, appraising the trial. The intention is to conceive a structure so that a platform is established for performing frequent advances over the prototype at the assigned task that is accustomed (Sadhasivam & Kalivaradhan, 2019; Suryawanshi et al, 2020).…”

Section: Machine Learningmentioning

confidence: 99%

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Naithani

Raiwani

2022

Expert Systems

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The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K-means, K-means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed.

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Section: Machine Learningmentioning

confidence: 99%

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Naithani

Raiwani

2022

Expert Systems

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“…Sentiment analyses are done with the help of the machine learning algorithms to classify the textual data detecting the polarity of the reviews about online products (amazon, IMBD, and any other dataset) using either machine learning methods or deep learning methods or in some cases integrating them. In the field of the sentiment analysis using the machine learning algorithms, the researchers in [4,5] focused on increasing the accuracy of the review classification. ey used the unigram and weighted unigram techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e classification is performed with finding the hyperplane that distinguishes between the two classes. On the other hand, [5] utilised the technique of "ensemble" machine learning algorithm that combines the predictions from the output of the classifiers NB and SVM together to produce more accurate predictions compared to an individual model. Dealing with the issue of performance, speed execution and accuracy created a better accurate system in contrast with the old systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews

Alharbi

Alghamdi

Alkhammash

et al. 2021

Mathematical Problems in Engineering

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Consumer feedback is highly valuable in business to assess their performance and is also beneficial to customers as it gives them an idea of what to expect from new products. In this research, the aim is to evaluate different deep learning approaches to accurately predict the opinion of customers based on mobile phone reviews obtained from Amazon.com. The prediction is based on analysing these reviews and categorizing them as positive, negative, or neutral. Different deep learning algorithms have been implemented and evaluated such as simple RNN with its four variants, namely, Long Short-Term Memory Networks (LRNN), Group Long Short-Term Memory Networks (GLRNN), gated recurrent unit (GRNN), and update recurrent unit (UGRNN). All evaluated algorithms are combined with word embedding as feature extraction approach for sentiment analysis including Glove, word2vec, and FastText by Skip-grams. The five different algorithms with the three feature extraction methods are evaluated based on accuracy, recall, precision, and F1-score for both balanced and unbalanced datasets. For the unbalanced dataset, it was found that the GLRNN algorithms with FastText feature extraction scored the highest accuracy of 93.75%. This result achieved the highest accuracy on this dataset when compared with other methods mentioned in the literature. For the balanced dataset, the highest achieved accuracy was 88.39% by the LRNN algorithm.

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“…The accuracy of the proposed algorithm is calculated and compared with the accuracy of another existing algorithm such as k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) applied on same data set (Sadhasivam and Kalivaradhan, 2019). For instance, for the proposed algorithm a keyword "java free online" has been put by the user as a query and in return, he is getting the link of an e-learning site containing the java content.…”

Section: Accuracy= (Tp+tn)/ (Tp+fp+tn+fn)mentioning

confidence: 99%

E-Learning Recommender System for Learners: A Machine Learning based Approach

Chaudhary¹,

Gupta²

2019

Int J Math, Eng, Manag Sci

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Web mining procedure helps the surfers to get the required information but finding the exact information is as good as finding a needle in a haystack. In this work, an intelligent prediction model using Tensor Flow environment for Graphics Processing Unit (GPU) devices has been designed to meet the challenges of speed and accuracy. The proposed approach is isolated into two stages: pre-processing and prediction. In the first phase, the procedure starts via looking through the URLs of various e-learning sites particular to computer science subjects. At that point, the content of looked through URLs are perused and after that from their keywords are produced identified with a particular subject in the wake of playing out the pre-processing of the content. Second phase is prediction that predicts query specific links of e-learning website. The proposed Intelligent E-learning through Web (IEW) has content mining, lexical analysis, classification and machine learning based prediction as its key features. Algorithms like SVM, Naïve Bayes, K-Nearest Neighbor, and Random Forest were tested and it was found that Random Forest gave an accuracy of 98.98%, SVM 42%, KNN 63% and Naïve Bayes 66%. Based on the results IEW uses Random forest for prediction.

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Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

Cited by 22 publications

References 6 publications

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews

E-Learning Recommender System for Learners: A Machine Learning based Approach

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