[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118828-en":3,"doc-seo-118828-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118828,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Product Review Classification Using Machine Learning and Statistical Data Analysis - Research overview","The paper implements and evaluates machine learning approaches for a product review dataset, covering binary classification, multi-class classification, and unsupervised clustering. Model quality is assessed across five classification tasks using 5-fold cross-validation on training data, with reported metrics including confusion matrices and ROC/AUC measures. The study also examines preprocessing strategies intended to strengthen predictive performance and compares strengths, limitations, and constraints of commonly used algorithms across the different tasks.","Dartmouth College  \nDartmouth Digital Commons  \n\n| Independent Student Projects and Publications | Other Student Work |\n| --- | --- |\n| Spring 5-7-2023\u003Cbr>Product Review Classification Using Machine Learning and Statistical Data Analysis\u003Cbr>Kajal Singh\u003Cbr>[kajal.singh.th@dartmouth.edu](kajal.singh.th@dartmouth.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.dartmouth.edu/student_projects](https://digitalcommons.dartmouth.edu/student_projects)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons |  |\n\nDartmouth Digital Commons Citation  \nSingh, Kajal, \"Product Review Classification Using Machine Learning and Statistical Data Analysis\" (2023) . Independent Student Projects and Publications. 6.  \n[https://digitalcommons.dartmouth.edu/student_projects/6](https://digitalcommons.dartmouth.edu/student_projects/6)  \nThis Article is brought to you for free and open access by the Other Student Work at Dartmouth Digital Commons. It has been accepted for inclusion in Independent Student Projects and Publications by an authorized administrator of Dartmouth Digital Commons. For more information, please contact [dartmouthdigitalcommons@groups.dartmouth.edu](dartmouthdigitalcommons@groups.dartmouth.edu).  \nProduct Review Classification Using Machine Learning and Statistical  \nData Analysis  \nKajal Singh  \nDartmouth College  \nHanover, NH, USA  \n[Kajal.singh.th@dartmouth.edu](Kajal.singh.th@dartmouth.edu)  \nAbstract  \nThe aim of the paper is to implement and analyze the machine learning models for product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The performance of the models over each of the five classification tasks is measured by the 5-fold cross-validation scores over the training data.  \n1. Introduction  \nMachine learning has revolutionized the field of data analysis and has proven to be an effective tool for solving complex classification and clustering problems. In recent years, there has been a significant increase in the availability of datasets, which has led to an explosion of research in the field of dataset classification using machine learning models.  \nIn this review paper, we aim to provide an overview of the most commonly used machine learning models for Binary, Multiclass and Clustering dataset classification, their strengths, weaknesses, and limitations based on the models’ performance. We will also discuss various preprocessing techniques that can be applied to improve the performance of these models.  \n.  \n2. Related Work  \nMachine learning has become an increasingly popular technique for solving classification problems. In classification problems, the goal is to predict a categorical label or class for a given input. For products review dataset, we have implemented the models to predict the overall rating based on the cutoff values and have clustered the dataset and measured model’s accuracy in terms of Silhouette score. The classification problems are ubiquitous in many fields, such as finance, healthcare, ande-commerce.  \n2.1 Binary Classification  \nBinary classification is a type of classification problem in which the goal is to predict one of two possible outcomes, such as 1 or 0, yes or no, true or false, or positive or negative. Binary classification is used in many fields, such as fraud detection, spam filtering, and medical diagnosis. There are several machine learning algorithms that can be used for binary classification, including logistic regression, support vector machines, random forest, decision trees, and neural networks.  \nIn this paper, we have implemented binary classification for 4 different cutoff values: 1,2,3,4 to predict the overall ratings: 0 or 1. The cutoff is not an input to the model, but to the experiment. For example, when cutoff=3, all samples with a rating \u003C= 3 will have label 0, and all samples with a rating > 3 have label 1. The model performance","cbCaisftyUfT7pzJ","https://ap.wps.com/l/cbCaisftyUfT7pzJ","pdf",250067,1,"English","en",105,"# Introduction\n# Related Work\n## Binary Classification\n## Multiclass Classification\n## Clustering","[{\"question\":\"What machine learning tasks does the project address for product reviews?\",\"answer\":\"It covers binary classification, multi-class classification, and clustering to analyze and categorize product reviews based on rating patterns and grouped similarity.\"},{\"question\":\"How is model performance evaluated in the paper?\",\"answer\":\"Performance is measured using 5-fold cross-validation scores on the training data for each of the classification tasks.\"},{\"question\":\"How does the binary classification setup work with rating cutoffs?\",\"answer\":\"Cutoff values define labels: for a given cutoff, ratings at or below the cutoff map to one class and ratings above the cutoff map to the other class, while the cutoff is used for the experiment rather than as a model input.\"}]","Product Review Classification Using Machine Learning and Statistical Data Analysis - 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