Sentiment Analysis of Product Reviews using Support Vector Machine Learning Algorithm

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

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Support vector machine learning algorithm and transduction

Computational Statistics ◽

10.1007/s001800050034 ◽

2000 ◽

Vol 15 (1) ◽

pp. 31-39 ◽

Cited By ~ 12

Author(s):

A. Gammermann

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithm ◽

Support Vector ◽

Machine Learning Algorithm

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Field measurements of suspended frazil ice. Part I: A support vector machine learning algorithm to identify frazil ice particles

Cold Regions Science and Technology ◽

10.1016/j.coldregions.2019.102812 ◽

2019 ◽

Vol 165 ◽

pp. 102812 ◽

Cited By ~ 1

Author(s):

Vincent McFarlane ◽

Mark Loewen ◽

Faye Hicks

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithm ◽

Field Measurements ◽

Support Vector ◽

Machine Learning Algorithm ◽

Frazil Ice ◽

Ice Particles

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Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7889.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2862-2867

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Feature Selection ◽

Sentiment Analysis ◽

Opinion Mining ◽

Classification Performance ◽

Support Vector ◽

Product Reviews ◽

Feature Selection Technique ◽

Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.

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Credit Card Fraud Detection

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0705009 ◽

2021 ◽

Vol 7 (05) ◽

pp. 60-62

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Credit Card ◽

Learning Algorithm ◽

Support Vector ◽

Machine Learning Algorithm ◽

Nearest Neighbour ◽

Decision Tree Algorithm ◽

Random Forest Algorithm ◽

Real World Problem

This research paper proposes a solution that should be deployed to identify whether the transaction is fraud or not. Although we know that most of the transaction takes place online meaning that this transaction can be theft on the go and will create problem to user therefore this paper focus on some particular machine learning algorithm for example Random forest Algorithm, Decision Tree Algorithm, Logistic Regression, Support Vector Machine, K Nearest Neighbour, XGBoost .Which aims at solving such kind of real-world problem.

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A Practical Fault Tolerance Approach in Cloud Computing Using Support Vector Machine

10.54646/bijscit.010 ◽

2020 ◽

pp. 45-49

Author(s):

Gajendra Sharma ◽

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Cloud Computing ◽

Fault Tolerance ◽

Learning Algorithm ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Tolerance Approach ◽

Systematic Solution

Fault tolerance is an important issue in the field of cloud computing which is concerned with the techniques or mechanism needed to enable a system to tolerate the faults that may encounter during its functioning. Fault tolerance policy can be categorized into three categories viz. proactive, reactive and adaptive. Providing a systematic solution the loss can be minimized and guarantee the availability and reliability of the critical services. The purpose and scope of this study is to recommend Support Vector Machine, a supervised machine learning algorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strength of machine learning algorithm with cloud computing.

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