[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123842-en":3,"doc-seo-123842-105":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},123842,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning Methodologies Based Improved Classification System for Sentiment Analysis of Tweets - Abstract","Sentiment analysis of tweets supports the study of public opinion, brand perception, and sentiment dynamics as social media volume continues to grow. The paper presents a comprehensive investigation of an enhanced machine learning methodology for tweet sentiment classification, combining natural language processing techniques with advanced machine learning. It motivates the work by outlining relevance across domains, reviewing limitations of conventional approaches, and identifying gaps in prior research. The study describes a full pipeline with tokenization, stop-word removal, stemming, TF-IDF and word embeddings, and an improved hybrid deep learning and ensemble approach, reporting higher accuracy and robustness.","Machine Learning Methodologies Based Improved Classification System for Sentiment Analysis of  \nTweets  \nPrasun Tripathi1, Dr. Mukesh Kumar2  \n1Research Scholar, Department ofCSE, Rabindra Nath Tagore University, Bhopal, India 2Associate Professor, Department of CSE, Rabindra Nath Tagore University, Bhopal, India  \nAbstract—: An increasingly important part of studying public opinion, sentiment patterns, and how people see brands is analyzing tweets for sentiment. The need for effective and precise sentiment analysis techniques is growing in tandem with the volume of social media data. This article details an extensive investigation into the planning, modeling, and evaluation of an enhanced machine learning approach to sentiment analysis of tweets. In order to achieve better results in sentiment classification, the suggested approach integrates the best of natural language processing methods with state-of-the-art machine learning algorithms. The paper begins by outlining the relevance and uses of sentiment analysis in different fields. It draws attention to the necessity for more reliable and precise methodology by discussing the problems with conventional sentiment analysis techniques. After that, the article dives into related research, looking at current state-of-the-art methods and finding holes that the suggested approach intends to fill. The methodology part explains how the sentiment analysis pipeline works. Tokenization, stop-word removal, and stemming are part of the data preparation steps that start it all. Word embeddings and TF-IDF are two of the feature extractions approaches that are investigated and contrasted. An enhanced machine learning algorithm integrating deep learning and ensemble learning is subsequently introduced in the article. The results show that the suggested methodology achieves better accuracy and resilience in sentiment classification than traditional sentiment analysis approaches, and it also elaborates on the model's architecture, training process, and strategies for optimizing performance parameters. The article emphasizes the model's capabilities in dealing with sentiment analysis problems such as context-specific language, sarcasm, and irony. Its capacity to manage massive datasets in real-time further demonstrates the efficacy of the suggested technique. This study article concludes by stressing the significance of sentiment analysis in gaining insight into public opinion and its function in governmental and corporate decision-making. Results from using the suggested methods to analyze the sentiment of tweets and other social media data are encouraging. In its last section, the paper proposes avenues for additional investigation into how to improve sentiment analysis methods and deal with new problems that are cropping up in the industry.  \nKeywords-Sentiment Analysis, Tweets, Machine Learning, Natural Language Processing, Deep Learning, Ensemble Learning  \nI. INTRODUCTION  \nTwitter in particular has grown into a potent medium for people to air their views, feelings, and thoughts on a broad variety of issues via social media. It is possible to learn how the public feels about certain things, people, and events by scouring the vast amounts of user-generated information on these sites. Understanding and interpreting this massive volume of textual data relies heavily on sentiment analysis, which is also called opinion mining. Determining if a text is inherently good, negative, or neutral is what it entails.  \nSentiment analysis has many different and wide-ranging uses. In the corporate world, sentiment analysis is useful for tracking how people feel about a brand, how satisfied customers are, and how to improve marketing and new product development strategies. For political scientists, it's a useful tool for gauging public opinion on various issues, politicians, and programs. In addition, financial markets have used sentiment research to forecast stock price swings according to investor mood.","cbCainp9XlxUveJE","https://ap.wps.com/l/cbCainp9XlxUveJE","pdf",378878,1,6,"English","en",105,"# Introduction\n## Uses of sentiment analysis\n## Traditional lexicon-based methods\n## Machine learning and NLP based approaches\n# Related work and methodology overview","[{\"question\":\"What problem does the paper address in tweet sentiment analysis?\",\"answer\":\"It targets the need for more accurate and reliable sentiment classification as social media data volume increases, focusing on improving methodology beyond conventional techniques.\"},{\"question\":\"How does the proposed approach prepare tweet data?\",\"answer\":\"The pipeline includes tokenization, stop-word removal, and stemming, followed by feature extraction using TF-IDF and word embeddings.\"},{\"question\":\"What techniques are used to improve classification performance?\",\"answer\":\"The paper introduces an enhanced machine learning approach that integrates deep learning with ensemble learning to improve accuracy and robustness against context, sarcasm, and irony.\"}]","Machine Learning Methodologies Based Improved Classification System for Sentiment Analysis of Tweets - 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