Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Karupusamy, Selvaraj; Maruthachalam, Sundaram; Mayilswamy, Suresh; Sharma, Shubham; Singh, Jujhar; Lorenzini, Giulio

doi:10.18280/ria.350601

Cited by 4 publications

(1 citation statement)

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“…Sentiment analysis, which is usually referred to as "opinion mining," is the research subject. Analysis is done on people's attitudes, views, feelings, assessments, and appraisals of goods, services, organizations, people, issues, events, subjects, and traits [14]. Sentiment analysis determines the text's relative polarity.…”

Section: Sentiment Analysismentioning

confidence: 99%

Fake Review Detection Using Machine Learning

Asaad,

Allami,

Ali

2023

RIA

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Online customer reviews have become an increasingly influential tool in shaping purchasing decisions. However, the growing impact of these reviews has led to a surge in the publication and promotion of fake reviews by some businesses, either to enhance their own product's reputation or to undermine their competitors. These counterfeit reviews can have an especially detrimental impact on small businesses, with even a single negative fake review capable of causing significant damage. In this context, the current study introduces a technique for classifying and identifying fake reviews using machine learning (ML) methodologies. The proposed algorithm was applied to the Yelp dataset for hotel services. The text was initially preprocessed through four stages: tokenization, normalization, stop word removal, and stemming. Subsequently, features were extracted using TFIDF techniques to leverage the benefits of sentiment analysis and to ascertain the presence of spam comments in the feature extraction approach. During the classification phase, the study employed three ML algorithms: Xgboost, a support vector classifier, and stochastic gradient descent. The proposed model was evaluated on both balanced and imbalanced datasets, using oversampling and undersampling techniques to determine its accuracy. The findings of this research hold promise for enhancing the credibility of online reviews and protecting businesses from the adverse effects of fake reviews. By unmasking fraudulent reviews, this study contributes to ensuring the integrity of online review platforms and safeguarding the interests of both businesses and consumers.

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Section: Sentiment Analysismentioning

confidence: 99%