Classification of Amazon Book Reviews Based on Sentiment Analysis

Srujan, K. S. S. Sai; Nikhil, Saragadam; Rao, Haidi; Karthik, K.; Harish, B. S.; Kumar, H. M. Keerthi

doi:10.1007/978-981-10-7512-4_40

Cited by 35 publications

(16 citation statements)

References 6 publications

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“…Random Forest (RF) is a classification algorithm that can be used to identify sentiment analysis [44], [45] and emotion analysis [46], [47] in numbers significant data. Random forest is a classification based on combining tree structures with training on the sample data they have.…”

Section: Methodsmentioning

confidence: 99%

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

2021

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All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.

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Section: Methodsmentioning

confidence: 99%

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

2021

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“…Classification of Reviews using KNN: After the features are extracted then the final classification is done using KNN algorithm. This method is non-parametric, it takes K closest training set as input and class membership as output [21]. For this the whole data set is divided into training and testing dataset then the classification algorithm is applied on training dataset.…”

Section: Fig 2 Research Methodologymentioning

confidence: 99%

A Consumer Behavior Prediction Method for E-Commerce Application

2019

IJRTE

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The consumer behavior analysis is the technique which is applied to analyze consumer behavior. The customer behavior analysis has the three steps which are pre-processing, feature extraction and classification for prediction. In the previous work, Naïve Bayes was applied for the consumer behavior analysis. In this work, hybrid classifier is designed for the customer behavior analysis using Decision Tree and KNN. The proposed method is implemented in anaconda python and results are compared with the previously used Naïve Bayes method, for this analysis consumer reviews from Amazon website are used.

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“…Using the content available in online book stores, several studies attempted to classify online book reviews based on sentiment polarities using machine learning models, with above 80% accuracy (Chiavetta et al , 2016; Srujan et al , 2018). Piryani et al (2018) also conducted sentiment analysis on online reviews collected from Amazon and Goodreads and found approximately 80% and 70% positive reviews on Amazon and Goodreads, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

What books will be your bestseller? A machine learning approach with Amazon Kindle

Lee

Kim

et al. 2021

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Purpose With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions. Design/methodology/approach The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used. Findings The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%. Originality/value This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

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Classification of Amazon Book Reviews Based on Sentiment Analysis

Cited by 35 publications

References 6 publications

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

A Consumer Behavior Prediction Method for E-Commerce Application

What books will be your bestseller? A machine learning approach with Amazon Kindle

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