[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118075-en":3,"doc-seo-118075-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118075,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Sentiment Analysis of Twitter Data Using Machine Learning Techniques - Twitter情感分析机器学习方法","Social media enables users to express thoughts and emotions continuously, creating large collections of text that reflect public reactions to real-world events. This paper performs sentiment analysis on Twitter by automatically categorizing tweets as positive, negative, or neutral using machine learning and natural language processing. Multiple models are evaluated on Twitter data, with text preprocessing to remove noise and numerical representation via vectorization and word embeddings. Performance is assessed using metrics including accuracy and F1 score, achieving the highest accuracy of 0.73 with the Bidirectional LSTM configuration.","Sentiment Analysis of Twitter Data Using Machine Learning Techniques  \nMantasha Khan 1 and Ankita Srivastava2  \n1 Student, Department of Computer Science & Engineering, Integral University, INDIA 2Assistant Professor, Department of Computer Science & Engineering, Integral University, INDIA  \n[1](1Corresponding Author: mantashakhan900.0@gmail.com)[Corresponding Author: mantashakhan900.0@gmail.com](1Corresponding Author: mantashakhan900.0@gmail.com)  \nReceived: 22-01-2024 Revised: 11-2-2024 Accepted: 28-02-2024  \nABSTRACT  \nIn the age of social media, it is more convenient for individuals to articulate their thoughts and emotions. Each day, they disseminate their perspectives and notions on various social media platforms about ongoing global events. On controversial issues, one can find a consensus of public feeling, whether positive or negative. Twitter functions as a demonstration of a social media platform where individuals participate in discussions about their perspectives. Twitter sentiment analysis examines the overall feeling or emotion expressed in tweets. It employs machine learning and natural language processing techniques to automatically categorize tweets as good, negative, or neutral depending on their content. It may be used for single tweets or a bigger dataset relating to a specific topic or event. Through the identification of these sentiments, machine learning endows us with an advantageous position in the analysis and prediction of said sentiments. Distinct machine learning models are utilized in this paper to scrutinize sentiments within Twitter data. The proposed system offers a comprehensive evaluation of the performance of various machine learning algorithms, including Vader, XGBoost with CountVectorizer, XGBoost with Gensim, Random Forest with CountVectorizer, Random Forest with Gensim, Single LSTM, and Bidirectional LTSM and Bidirectional LTSM gives highest accuracy of .73.  \nKeywords-- Crisis Management, LSTM, Sentimental Analysis, Tokenization, Vader  \nI. INTRODUCTION  \nTwitter has emerged as a prominent platform for discussion of intense emotions, making it a valuable source of information for analyzing sentiments. Sentiment analysis is the technique of examining text to detect its underlying emotional tone. With the rise of social media platforms like Twitter, analysis of sentiment has become an essential tool for businesses, associations, and governments seeking to comprehend public opinion and form well-informed perspectives [11] . Natural Language Processing (NLP) methods are extensively employed for sentiment analysis as they enable machines to comprehend and interpret human language [12]. NLP techniques can  \nanalyze tweets in real time, identify the sentiment conveyed in tweets, and provide insights into prevailing trends and patterns in public sentiment [13] . Machine learning algorithms, which fall under the umbrella of NLP, can acquire knowledge from vast datasets and accurately predict the sentiment of new tweets. In this investigation, we aim to assess the efficacy of ML systems in conducting sentiment analysis on Twitter using NLP methodologies. To classify tweets as favorable, negative, or neutral., we will utilize a dataset that includes tweets from the opening day of the “FIFA World Cup 2022”, held in Qatar. This dataset encompasses information such as the date of creation, number of likes, tweet source, tweet content, and sentiment. We will preprocess this data to eliminate any noise and subsequently use machine learning methods such as Vader, XGBoost, Random Forest, and LSTM (Long Short-Term Memory) . To improve accuracy, we use count vectorizers and genism in our models. Machines cannot interpret letters or words. When dealing with text data, we must represent it numerically so that the machine can interpret it. Count vectorizer is a method for translating text to numerical data. Gensim is an open-source Python package for NLP. The Gensim package this allows us to create word embe","cbCail3XtABOaD3S","https://ap.wps.com/l/cbCail3XtABOaD3S","pdf",617420,1,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"What is the goal of the sentiment analysis in this paper?\",\"answer\":\"The paper aims to classify tweets into positive, negative, or neutral sentiment using machine learning and NLP techniques.\"},{\"question\":\"Which machine learning models are compared for Twitter sentiment classification?\",\"answer\":\"The study evaluates several models including Vader, XGBoost with CountVectorizer or Gensim, Random Forest with CountVectorizer or Gensim, Single LSTM, and Bidirectional LSTM.\"},{\"question\":\"How is text from tweets prepared for model training?\",\"answer\":\"The work preprocesses tweets to remove noise and converts text into numerical representations using CountVectorizer and Gensim-based word embeddings.\"}]","Sentiment Analysis of Twitter Data Using Machine Learning Techniques - 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