Sentiment Analysis on Product Reviews Using Machine Learning Techniques

Jagdale, Rajkumar S.; Shirsat, Vishal S.; Deshmukh, Sachin N.

doi:10.1007/978-981-13-0617-4_61

Cited by 159 publications

(56 citation statements)

References 6 publications

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“…For this process machine learning algorithm is used called Decision Tree which is supervised learning algorithm. Each sentence is analyzed and sentiment score is given [22].…”

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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“…For this process machine learning algorithm is used called Decision Tree which is supervised learning algorithm. Each sentence is analyzed and sentiment score is given [22].…”

Section: Fig 2 Research Methodologymentioning

confidence: 99%

A Consumer Behavior Prediction Method for E-Commerce Application

2019

IJRTE

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“…In 2019, Rajkumar S. et al [20] proposed to analyze the sentiments and then classify them by using machine learning algorithms Naïve Bayes (NB) and support vector machine (SVM) with dictionary-based model based one lexicon-based method. NB classifier obtained 98.17% accuracy for camera reviews and SVM achieved 93.54% accuracy for camera reviews.…”

Section: Related Workmentioning

confidence: 99%

Analyzing sentiment system to specify polarity by lexicon-based

Hamed¹,

Abbas

Sadiq³

2021

Bulletin EEI

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Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.

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“…Sentiment analysis is performed on three levels i.e., a) document level, b) sentence level and phrase level [3].The document level sentiment analysis focuses on classifying the entire document as positive or negative. In the document level classification, a single review of a single topic is considered.…”

Section: A Different Levels Of Sentiment Analysismentioning

confidence: 99%

A Sentiment Polarity Categorization Technique for Online Product Reviews

Kausar

Waqas

et al. 2020

IEEE Access

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Sentiment analysis is also known as opinion mining which shows the people's opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent. INDEX TERMS Sentiment, opinion mining, social media, natural language processing.

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Sentiment Analysis on Product Reviews Using Machine Learning Techniques

Cited by 159 publications

References 6 publications

A Consumer Behavior Prediction Method for E-Commerce Application

A Consumer Behavior Prediction Method for E-Commerce Application

Analyzing sentiment system to specify polarity by lexicon-based

A Sentiment Polarity Categorization Technique for Online Product Reviews

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