[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118860-en":3,"doc-seo-118860-105":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},118860,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Based Twitter Sentiment Analysis and User Influence","Machine learning techniques are used to analyze Twitter content and determine sentiment expressed in tweets as positive, negative, or neutral. The research targets both sentiment classification and the identification of the most influential users within a specific topic using a popularity-based ranking approach. The method combines NLP preprocessing, sentiment lexicons, and multiple machine learning classifiers, then evaluates performance on a large dataset of US airlines tweets. Logistic regression achieves the highest accuracy among tested models.","Machine Learning Based Twitter Sentiment Analysis  \nand User Influence  \nMrs. Ragini Krishna 1 , Dr. Prashanth C.M.2  \n1Assistant Professor,  \nDepartment of Information Science & Engineering  \nSri Krishna Institute of Technology  \nBangalore 560090, India  \n[ragini.krishna@gmail.com](ragini.krishna@gmail.com)  \n2Principal  \nMangalore Institute of Technology and Engineering  \nMangalore – 574225, India  \n[prashanth.ait@gmail.com](prashanth.ait@gmail.com)  \nAbstract—The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral.  \nIn this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic.  \nThe proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy.  \nThe proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively.  \nKeywords: sentiments, natural language processing, AdaBoost, gradient boosting, Naïve Baye’s, Decision Trees, influential user.  \nI. INTRODUCTION  \nThe use of social media has become a prevalent part of modern society, with platforms like Twitter allowing users to share their thoughts, opinions, and experiences with the world in real-time. However, with the vast amount of content being produced every second, it can be challenging to stand out and gain a significant following.  \nIn recent years, there has been growing interest in measuring social media influence, particularly on Twitter. Influence refers to a user's ability to affect the behavior, attitudes, and opinions of others on the platform. Measuring influence can provide valuable insights into user’s behavior and help individuals and organizations make informed decisions on social media marketing, advertising, and content creation strategies.  \nWith the activity levels and the follower count of the users, machine learning models can accurately predict a user's level of influence on the platform. This can help users identify areas for improvement and tailor their content to increase their influence. Machine learning has proven to be a powerful tool for predicting social media influence by analyzing various user-generated data such as the post frequency and engagement.  \nThe motivation behind this research is to contribute to the growing body of knowledge on measuring social network influence and to provide a more accurate and reliable metho","cbCaipPxa1qp8o9v","https://ap.wps.com/l/cbCaipPxa1qp8o9v","pdf",527410,1,7,"English","en",105,"# Introduction\n## Measuring social media influence on Twitter\n## Motivation and paper organization\n# Literature Reviews","[{\"question\":\"How does the research determine sentiment in tweets?\",\"answer\":\"It uses NLP techniques, sentiment lexicons, and machine learning classifiers to label tweets as positive, negative, or neutral.\"},{\"question\":\"How are influential users identified in a topic?\",\"answer\":\"After sentiment classification, the approach calculates user popularity and ranks users to select the top 10 most influential accounts for the given topic.\"},{\"question\":\"Which machine learning model performed best in the experiments?\",\"answer\":\"Logistic regression achieved the highest accuracy compared with other models such as Naive Bayes, SVM, decision trees, gradient boosting, AdaBoost, KNN, and random forest.\"}]","Machine Learning Based Twitter Sentiment Analysis and User Influence | 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does the research determine sentiment in tweets?","Question",{"text":75,"@type":76},"It uses NLP techniques, sentiment lexicons, and machine learning classifiers to label tweets as positive, negative, or neutral.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are influential users identified in a topic?",{"text":80,"@type":76},"After sentiment classification, the approach calculates user popularity and ranks users to select the top 10 most influential accounts for the given topic.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best in the experiments?",{"text":84,"@type":76},"Logistic regression achieved the highest accuracy compared with other models such as Naive Bayes, SVM, decision trees, gradient boosting, AdaBoost, KNN, and random 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