[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123207-en":3,"doc-seo-123207-105":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},123207,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Hybrid approach for tweets similarity classification founded on case based reasoning and machine learning techniques","Twitter sentiment analysis has become a widely researched task due to the large volume of publicly shared opinions on social platforms. This study introduces a hybrid method combining dynamic case based reasoning, multinomial logistic regression, TF-IDF-based feature weighting, and a multi-agent system to compute tweet polarity and score. Similar tweets are retrieved using content similarity with K-nearest neighbors and features extracted for classification. Experiments focus on Covid-19 tweets using a public Twitter dataset and an adaptive, generic pipeline for tracking users’ behavior.","Hybrid approach for tweets similarity classification founded on case based reasoning and machine learning techniques  \nIsmail Bensassi1, Mohamed Kouissi2, Oussama Ndama1, El Mokhtar En-Naimi1, Abdelhamid Zouhair1  \n1DSAI2S Research Team, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, Tetouan, Morocco 2DSAI2S Research Team, La Faculté Polydisciplinaire de Larache (FP of Larache), Abdelmalek Essaâdi University, Tetouan, Morocco  \n\n| Article history:\u003Cbr>Received Mar 9, 2024 Revised Oct 1, 2024 Accepted Oct 17, 2024 | Twitter sentiment analysis becomes a popular research subject in the last decade. It aims to extract sentiments of users through their public opinion about a given topic. This article proposes a hybrid approach for Twitter sentiment analysis founded on dynamic case based reasoning (DCBR), multinomial logistic regression machine learning algorithm and multi-agent system. Our approach proposes a method to find similar tweets based on content similarity measure using the scientific measurement of keyword weight term frequency-inverse document frequency (TF-IDF) . This approach includes gathering and pre-processing tweets, getting score and polarity of tweets, the use of multinomial logistic regression machine learning algorithm to classify our tweets into various classes, using the feature extraction method to extract useful features and then the K-nearest neighbors (KNN) algorithm to make it easier to find similar tweets to our tweet target case. This approach is adaptive and generic and able to track users' tweet to predict their behavior and sentiments in critical situations and delivering personalized content. The current study focuses on Covid-19 tweets, and a public Twitter dataset is used for this purpose.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Dynamic case based reasoning Machine learning\u003Cbr>Multi agents system\u003Cbr>Term frequency-inverse document frequency\u003Cbr>Tweets similarity classification |  |\n\nCorresponding Author:  \nIsmail Bensassi  \nDSAI2S Research Team, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University Tetouan, Morocco  \n[Email: bensassi.ismail@gmail.com](Email: bensassi.ismail@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nSocial network plays an important role for most people, it becomes an indispensable part for human interactions. It represents a relevant way for expressing opinions, thoughts, and sharing more personal emotions and sentiments about various topics as stated in [1]-[4] . Twitter is a widely used social networking platform that generates a significant amount of data from tweets.  \nIn recent years, several studies have been done on Twitter sentiment analyses using big social data by gathering and classifying users’ opinions on a topic. Those studies encompass many disciplines, like Covid-19, Covid-19 vaccine, and elections commercial activities. Twitter sentiment analyses is a process which determines the sentiment orientation of a text. The main idea of Twitter sentiment analysis becomes a question of whether a tweet is expressing positive, negative, or neutral towards the discussed subject. During Covid-19 pandemic, an increasing number of people used twitter platform to share their feelings with others [5] . So, feeling analysis has become a frequent research topic [6] . To help decision makers analyze users’ opinions and their reactions related to tweets content, and to predict their behavior and sentiments based on past experiences, we propose then a hybrid approach for Twitter sentiment analysis based on dynamic case based reasoning (DCBR), machine learning algorithms, natural language processing, and  \nmulti-agent system. This approach proposes an adaptive system for sentiment analysis classification to ensure a personalized follow-up of users in critical situations.  \nSeveral studies have been focused on the analysis of data from social networking platform","cbCaiiCqVq67W2Tf","https://ap.wps.com/l/cbCaiiCqVq67W2Tf","pdf",4314167,1,9,"English","en",105,"# Abstract\n# Introduction\n## Problem background and motivation\n## Related work and research gap\n## Proposed hybrid approach","[{\"question\":\"What hybrid components are used for tweets similarity classification?\",\"answer\":\"The method combines dynamic case based reasoning with multinomial logistic regression, TF-IDF for feature weighting, and a multi-agent system for sentiment-oriented processing.\"},{\"question\":\"How does the approach find similar tweets?\",\"answer\":\"It computes content similarity using a TF-IDF-based keyword weighting scheme, then uses feature extraction and K-nearest neighbors to retrieve tweets most similar to the target.\"},{\"question\":\"What dataset and scenario does the study focus on?\",\"answer\":\"The study targets Covid-19 tweets and uses a public Twitter dataset to evaluate the proposed sentiment and similarity classification pipeline.\"}]","Hybrid approach for tweets similarity classification founded on case based reasoning and machine learning techniques | 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hybrid components are used for tweets similarity classification?","Question",{"text":75,"@type":76},"The method combines dynamic case based reasoning with multinomial logistic regression, TF-IDF for feature weighting, and a multi-agent system for sentiment-oriented processing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach find similar tweets?",{"text":80,"@type":76},"It computes content similarity using a TF-IDF-based keyword weighting scheme, then uses feature extraction and K-nearest neighbors to retrieve tweets most similar to the target.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and scenario does the study focus on?",{"text":84,"@type":76},"The study targets Covid-19 tweets and uses a public Twitter dataset to evaluate the proposed sentiment and similarity classification 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