[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124621-en":3,"doc-seo-124621-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124621,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",6,"Technology","Effective Feature Selection Methods for User Sentiment Analysis using Machine Learning","Text classification assigns documents to predefined categories by training machine learning models on labeled datasets so they can predict labels for unseen texts. Feature selection is crucial in this workflow because it highlights the most informative words or phrases, improving predictive quality while lowering dataset dimensionality. The study presents an approach for extracting aspect terms from product reviews using Gini index and information gain within a wRMR framework, then evaluates multiple machine learning classifiers. Experiments with customer testimonials show improved aspect-term extraction over traditional and state-of-the-art baselines.","Effective Feature Selection Methods for User Sentiment Analysis using Machine Learning  \nSofiya S. Mujawar1, Dr. Pawan R. Bhaladhare2  \n1Phd scholar, Department of Computer science and Engineering, Sandip University, Nashik  \n2Professor, Department of Computer science and Engineering, Sandip University, Nashik  \n[sofiyamujawar01@gmail.com](sofiyamujawar01@gmail.com1)[1](sofiyamujawar01@gmail.com1), [pawan.bhaladhare@sandipuniversity.edu.in](pawan.bhaladhare@sandipuniversity.edu.in2)[2](pawan.bhaladhare@sandipuniversity.edu.in2)  \nAbstract  \nText classification is the method of allocating a particular piece of text to one or more of a number of predetermined categories or labels. This is done by training a machine learning model on a labeled dataset, where the texts and their corresponding labels are provided. The model then learns to predict the labels of new, unseen texts. Feature selection is a significant step in text classification as it helps to identify the most relevant features or words in the text that are useful for predicting the label. This can include things like specific keywords or phrases, or even the frequency or placement of certain words in the text. The performance of the model can be improved by focusing on the features that are most important to the information that is most likely to be useful for classification. Additionally, feature selection can also help to reduce the dimensionality of the dataset, making the model more efficient and easier to interpret. A method for extracting aspect terms from product reviews is presented in the research paper. This method makes use of the Gini index, information gain, and feature selection in conjunction with the Machine learning classifiers. In the proposed method, which is referred to as wRMR, the Gini index and information gain are utilized for feature selection. Following that, machine learning classifiers are utilized in order to extract aspect terms from product reviews. A set of customer testimonials is used to assess how well the projected method works, and the findings indicate that in terms of the extraction of aspect terms, the method that has been proposed is superior to the method that has been traditionally used. In addition, the recommended approach is contrasted with methods that are currently thought of as being state-of-the-art, and the comparison reveals that the proposed method achieves superior performance compared to the other methods. In general, the method that was presented provides a promising solution for the extraction of aspect terms, and it can also be utilized for other natural language processing tasks.  \nKeyword- Product review, feature selection, classification, Gini Index, Information Gain.  \nI. Introduction  \nText classification is a complex field due to its highdimensional nature. This phenomenon can cause the number of samples that are required to estimate the distribution of probability to increase exponentially. This curse of dimensionality can affect the generalisation and overfitting performance of the program. It is therefore important that the classification process is carried out in a way that minimizes the complexity of its feature space. The three main categories of classification are: wrapper, embedded, and filter. The first step in the procedure is to preprocess the data to remove theirrelevant features. One of the most common methods for implementing classification is by using simple methods that are very efficient when it comes to computational resources. However, these methods may not take into account all the necessary factors when it comes to the algorithm being used for the classification process. On the otherhand, wrapper methods require a lot of computation[1], [2] .  \nIn the training phase, the embedded methods are utilized to incorporate the learning algorithm and the selected features.  \nThe hybrid, embedded, and wrapper approaches use learning models in order to improve their accuracy and reduce the compu","cbCaisxygV9c473R","https://ap.wps.com/l/cbCaisxygV9c473R","pdf",424959,1,9,"English","en",105,"# Introduction\n## Text classification and high-dimensionality\n## Feature selection strategies (filter, wrapper, embedded, hybrid)\n## Dimensionality reduction approaches (PCA, LDA)\n## Correlation-based feature selection and mutual dependence\n## Gini index and information gain","[{\"question\":\"Why is feature selection important in user sentiment text classification?\",\"answer\":\"Feature selection identifies the most relevant words or phrases for predicting labels. It can also reduce dimensionality, making models more efficient and easier to interpret.\"},{\"question\":\"What feature-selection signals are used in the proposed wRMR approach?\",\"answer\":\"The proposed method uses the Gini index and information gain to perform feature selection before applying machine learning classifiers.\"},{\"question\":\"How is the proposed method evaluated for aspect-term extraction?\",\"answer\":\"Evaluation uses a set of customer testimonials and compares performance against traditional methods and state-of-the-art baselines, showing superior aspect-term extraction results.\"}]","Effective Feature Selection Methods for User Sentiment Analysis using Machine Learning | PDF",1785893362,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"effective-feature-selection-methods-for-user-sentiment-analysis-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/effective-feature-selection-methods-for-user-sentiment-analysis-using-machine-learning/124621/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is feature selection important in user sentiment text classification?","Question",{"text":75,"@type":76},"Feature selection identifies the most relevant words or phrases for predicting labels. It can also reduce dimensionality, making models more efficient and easier to interpret.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What feature-selection signals are used in the proposed wRMR approach?",{"text":80,"@type":76},"The proposed method uses the Gini index and information gain to perform feature selection before applying machine learning classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed method evaluated for aspect-term extraction?",{"text":84,"@type":76},"Evaluation uses a set of customer testimonials and compares performance against traditional methods and state-of-the-art baselines, showing superior aspect-term extraction results.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]