[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124563-en":3,"doc-seo-124563-105":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},124563,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Comparative study on sentimental analysis using machine learning techniques - read","Advancement of the Internet drives rapid growth of unstructured textual data from social media, product reviews, and event discussions. Sentiment analysis addresses the need to determine whether audiences or potential buyers express positive, negative, or neutral opinions. The study compares multiple machine learning approaches, including CNN, Naive Bayes, Decision Tree, XGBoost, and Logistic Regression, to classify sentiments from text comments. Results evaluate performance using different metrics, showing XGBoost achieves the best overall performance among the compared methods.","[https://doi.org/10.22581/muet1982.2301.19](https://doi.org/10.22581/muet1982.2301.19)  \n2023, 42(1) 207-215  \nComparative study on sentimental analysis using machine learning techniques  \nMurali Krishna Enduri a, Abdur Rashid Sangi b,* , Satish Anamalamudi a, R. Chandu Badrinath Manikanta a, K. Yogeshvar Reddy a, P. Lovely Yeswanth a, S. Kiran Sai Reddy a, Asish Karthikeya aa Department of Computer Science and Engineering, SRM University-AP,Amaravati, GunturIndia  \nb Department of Computer Science, College of Science and Technology, Wenzhou-Kean University, Ouhai, Wenzhou Zhejiang China  \n* Corresponding author: Abdur Rashid Sangi, Email: [sangi_bahrian@yahoo.com](sangi_bahrian@yahoo.com)  \nReceived: 26 October 2022, Accepted: 15 December 2022, Published: 01 January 2023  \nK E Y W O R D S  \nSentimental Analysis Machine Learning Textual Opinions  \nA B S T R A C T  \nWith the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.  \n1. Introduction  \nAccess Sentiment analysis is study of the distinct existence of people such as opinion, emotions, thoughtsand reviews of the people. Mainly it determines the quality or quantity of the article, news, service, product etc by using the negative, neutral, and positive comments of the user. In other words, sentiment analysis is analyze the user's comments and information and gives better knowledge and idea about the product or service to new user. It means, rating, comments and reviews of product by user's inputs a high impact while choosing any product online by other user. Different user have various opinions. So in  \none case, a person treated as negative and it will betaken as positive in another case. A lot of the mixed opinions can be misleading so that why sentiment analysis comes into picture. This helps them to determine their business model by better understanding the general reaction into their products and their business stand in the market. Furthermore, it allows to understand the consumer’s needs and wants in the better possible way [6,15,25,19] . Reviews of movie plays an important role in gauge the performance and publicity of a movie. While rating the movie, the writer tells about the status, failure or success of a movie quantitatively to the end users. A textual movie review helps to the end users about the the merits and demerits  \nof the movie. Furthermore, the deeper analysis of a movie review can help the end users to conclude whether the general expectations of the audience can be attained by the reviewer. In this paper, authors also to propose an sentiment analysis on movie reviews which are given by reviewers and gives information of the overall output of the movie. In other words, the conclusion would be whether the movie is good or bad based on the story or other technical aspects. Further, authors goal to extract the connection or correlation of the movie keywords in the review process to identify or predict the","cbCaiuIq4T8dm6vz","https://ap.wps.com/l/cbCaiuIq4T8dm6vz","pdf",455459,1,9,"English","en",105,"# Introduction\n## Types of sentiment analysis\n## Applications\n### Product design and improvement phase","[{\"question\":\"What problem does sentiment analysis address in this study?\",\"answer\":\"It classifies unstructured textual opinions from sources like social media and product reviews into positive, negative, or neutral categories.\"},{\"question\":\"Which machine learning models are compared for sentiment analysis?\",\"answer\":\"The document compares CNN, Naive Bayes, Decision Tree, XGBoost, and Logistic Regression.\"},{\"question\":\"What conclusion does the study reach about model performance?\",\"answer\":\"Across the evaluated performance metrics, XGBoost outperforms the other compared methods.\"}]","Comparative study on sentimental analysis using machine learning techniques - read | PDF",1785893012,23,{"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},"comparative-study-on-sentimental-analysis-using-machine-learning-techniques-read","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-study-on-sentimental-analysis-using-machine-learning-techniques-read/124563/",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},"What problem does sentiment analysis address in this study?","Question",{"text":75,"@type":76},"It classifies unstructured textual opinions from sources like social media and product reviews into positive, negative, or neutral categories.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared for sentiment analysis?",{"text":80,"@type":76},"The document compares CNN, Naive Bayes, Decision Tree, XGBoost, and Logistic Regression.",{"name":82,"@type":73,"acceptedAnswer":83},"What conclusion does the study reach about model performance?",{"text":84,"@type":76},"Across the evaluated performance metrics, XGBoost outperforms the other compared methods.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]