Twitter Sentiment Analysis Using Dempster Shafer Algorithm Based Feature Selection And One Against All Multiclass Svm Classifier
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Twitter Sentiment Analysis Using Dempster Shafer Algorithm Based Feature Selection and One Against All Multiclass SVM Classifier
Rapid development of social media and internet technologies has conquered much intention on sentiment analysis. Twitter is one of the social media used by numerous users about some subject matter in the form of tweets. Twitter Sentiment Analysis (TSA) is the way of finding sentiments and opinions in the tweets. Still, achieving high accuracy in TSA is difficult due to characteristics of twitter data such as spelling errors, abbreviation and special characters. Therefore, our main intention is to attain high accuracy in TSA. To achieve this intention, we majorly concentrated on five processes: Data cleaning, Preprocessing, Feature extraction, Feature Selection and Classification. In the data cleaning stage, we perform four major processes: URL removal, Username Removal, Punctuation Removal and Spell Correction. By executing the data cleaning process, this work enhances the efficacy of TSA. To increase the accuracy in TSA, we adopt preprocessing where tokenization, stop word removal, lemmatization and stemming, acronyms expansion, slangs correction, split attached word, and POS tagging. In order to improve the classification accuracy, we execute the feature extraction process where eight features are extracted. A key bottleneck in TSA is a huge amount of data which makes difficulties in the training of ML during sentiment classification. To tackle this hurdle, we select the best features from the extracted features using the Dempster Shafer algorithm. Sentiments are classified using the One against All-Multiclass Support Vector Machine (OA2 -SVM) algorithm. It classifies sentiments into five classes: Strongly Positive, Strongly Negative, Positive, Negative and Neutral. We implement these processes using public tweets collected from the open repository. The results obtained from the simulation are auspicious in terms of upcoming metrics including, Accuracy, Precision, Recall, F-Measure and Error Rate. From the comparison results, it perceived that our method enhances 25% in Accuracy and Precision, 30% in Recall, 20% in F-Measure and reduces 29% in error rate compared to the existing methods including LAN2 FIS, GA and HCS.
Progressive Computational Intelligence, Information Technology and Networking
Progressive Computational Intelligence, Information Technology and Networking presents a rich and diverse collection of cutting-edge research, real-world applications, and innovative methodologies spanning across multiple domains of computer science, artificial intelligence, and emerging technologies. This comprehensive volume brings together different scholarly chapters contributed by researchers, practitioners, and thought leaders from around the globe. The book explores a wide array of topics including—but not limited to—machine learning, deep learning, cloud computing, cybersecurity, Internet of Things (IoT), blockchain, natural language processing, image processing, and data analytics. It addresses the practical implementation of technologies in sectors such as healthcare, agriculture, education, smart cities, environmental monitoring, finance, and more. Each chapter delves into specific challenges, frameworks, and experimental outcomes, making this book an essential reference for academicians, researchers, industry professionals, and students who aim to stay ahead in the rapidly evolving digital world.
Computational Intelligence and Healthcare Informatics
COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.