Sentiment Analysis of Social Media Network Using Random Forest Algorithm

Karthika, P.; Murugeswari, R.; Manoranjithem, R.

doi:10.1109/incos45849.2019.8951367

Cited by 72 publications

(25 citation statements)

References 1 publication

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“…Traditional machine learning techniques, like Naive Bayes (Mubarok et al 2017; Anand and Naorem 2016), Decision Trees/Random Forest (Fitri et al 2019;Karthika et al 2019) and SVM (Pannala et al 2016;Wilson et al 2005;Tripathy et al 2016) have been widely used in the past. Esuli et al (2020) proposes an approach for cross-lingual sentiment quantification based on Structural Correspondence Learning (SCL) which is a technique that can be applied to different kinds of classifiers to transfer knowledge through a mapping between pivot terms of two feature spaces.…”

Section: Machine Learningmentioning

confidence: 99%

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

D’Aniello

Rocca

2022

Artif Intell Rev

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The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.

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Section: Machine Learningmentioning

confidence: 99%

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

D’Aniello

Rocca

2022

Artif Intell Rev

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“…The results for the word education were mostly positive. Karthika et al [18] evaluated different models and the experiments were conducted to find the best classifier to analyze the reviews from shopping site amazon. Based on those reviews the product is classified as positive, negative, or neutral.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive analysis of consumer decisions on Twitter dataset using machine learning algorithms

Vigneshwaran

Prasath²,

Manickavasagam³

et al. 2022

IJ-AI

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An exponential growth posting on the web about the product reviews on social media, there has been a great deal of examination being done on sorting out the purchasing behaviors of the client. This paper depends on utilizing twitter for sentiment analysis to comprehend the customer purchasing behavior. There has been a significant increase in e-commerce, particularly in persons purchasing products on the internet. As a result, it becomes a fertile hotspot for opinion analysis and belief mining. In this investigation, we look at the problem of recognizing and anticipating a client's purchase goal for an item. The sentiment analysis helps to arrive at a more indisputable outcome. In this study, the support vector machine, naive Bayes, and logistic regression methods are investigated for understanding the customer's sentiment or opinion on a specific product. These strategies have been demonstrated to be genuinely for making predictions using the analysis models which examine the client's conclusion/sentiment the most precisely. The exactness for each machine learning algorithm will be analyzed and the calculation which is the most precise would be viewed as ideal.

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“…The sentiment analysis method [11] first performed the steps of data preprocessing including removal of stop words, punctuation marks, alpha-numeric character, HTML-tags, de-duplication, stemming and lemmatization; then text feature extraction using Term Frequency/Inverse Document Frequency (TF/IDF); then data splitting into training and testing; and finally the classification using KNN, LR and SVM with 5-fold cross validation.Next method [12] collected the data; computed the co-variance and correlation on it; selected the dependent and independent variables; pre-processed the data; applied the linear transformation, standardization, normalization and data mining; extracted the features; and finally classified them along with the detection of fake reviews.Another method [13] also began with the collection of online product reviews using web scraping, then it pre-processed it, extracted the features, and classified them.It replaced the output layer of the Convolution Neural Networks (CNN) by SVM.The method followed the steps Copyright © 2021 MECS I.J. Engineering and Manufacturing, 2021, 2, 40-52 of data collection, pre-processing, feature selection, detection process, and sentiment classification with K-fold cross validation [14].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-based Approach

Dadhich¹,

Thankachan²

2021

IJEM

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In recent years, the retail market industry has taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations. The opinion summarization and classification systems extract and identify a range of opinions about different online available products in a large text-based review set. This paper addresses and reviews the concepts of automatic identification of the sentiments expressed in the English text for Amazon and Flipkart products using Random Forest and K-Nearest Neighbor techniques. It presents a detailed comparative study of such existing sentiment analysis algorithms and methodologies on the basis of five key parameters. It results in evaluating their performance in terms of parameter usage and contributions. The paper also discusses their experimental results and challenges found. Therefore, this study shows the maximum usage of feature extraction, positive-negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms.

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Sentiment Analysis of Social Media Network Using Random Forest Algorithm

Cited by 72 publications

References 1 publication

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

A comprehensive analysis of consumer decisions on Twitter dataset using machine learning algorithms

Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-based Approach

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