Download or read book An Enhanced Ensemble Classifier Framework for Sentiment Analysis of Social Media Issues written by Talha Ahmed Khan and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment Analysis is the study of determining an author's opinion from written text using artificial intelligence and data mining techniques. In this thesis, three different sentiment analysis techniques; Naïve Bayes Classification, Support Vector Machine and Ensemble Classification are studied and applied to social media datasets for extracting opinions. One of the uses of sentiment analysis is to act as a feedback mechanism to aid in decision making. In this thesis a Probabilistic Feature Weighting (PFW) technique is proposed using the principle of the Naïve Bayes Classifier and Bayesian Probability. The PFW helps in ranking the documents into further sub-categories and is useful to compare features and their importance in sentiment classification. An Enhanced Ensemble Classifier Framework (EECF) is also developed based on the PFW technique. The Enhanced Ensemble Classifier increases the accuracy of the system compared to the existing techniques. Social media documents consist of a smaller number of words and often lack formal use of language. As such, social media requires more sophisticated techniques to establish sentiment. EECF helps in classifying shorter documents that have a smaller number of features such as Twitter posts. The development of the Enhanced Ensemble Classifier is a contribution in the sentiment analysis domain. The proposed PFW technique provides an alternative method to investigate features and classify sentiment into sub-categories beyond positive and negative sentiment. The Enhanced Ensemble Classifier that utilizes the PFW is shown to improve the determination of sentiment.

Download or read book Social Media Sentiment Analysis with a Deep Neural Network written by Ahmed Sulaiman M. Alharbi and published by . This book was released on 2019 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that also incorporate user behavioral information within a given document (tweet). Within these frameworks, there are several models based on a variety of neural network architectures. Each of these models is trained on a specific aspect of user behavior. Then, the frameworks exploit these multi-aspect learning models to jointly take on a mutual task (the sentiment analysis task). The results of the preliminary experiments, which are reported in [1]-[3], demonstrate that going beyond the content of a document is beneficial in sentiment classification, because it provides the classifier with a deeper understanding of the task.

Download or read book Deep Learning Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer Nature. This book was released on 2020-01-24 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

Download or read book Sentiment Analysis written by Bing Liu and published by Cambridge University Press. This book was released on 2020-10-15 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.

Download or read book Handbook of Research on Emerging Trends and Applications of Machine Learning written by Solanki, Arun and published by IGI Global. This book was released on 2019-12-13 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.

Download or read book Progresses in Artificial Intelligence and Neural Systems written by Anna Esposito and published by Springer Nature. This book was released on 2020-07-09 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the current advances in artificial intelligence and neural nets. Artificial intelligence (AI) methods have shown great capabilities in modelling, prediction and recognition tasks supporting human–machine interaction. At the same time, the issue of emotion has gained increasing attention due to its relevance in achieving human-like interaction with machines. The real challenge is taking advantage of the emotional characterization of humans’ interactions to make computers interfacing with them emotionally and socially credible. The book assesses how and to what extent current sophisticated computational intelligence tools might support the multidisciplinary research on the characterization of appropriate system reactions to human emotions and expressions in interactive scenarios. Discussing the latest recent research trends, innovative approaches and future challenges in AI from interdisciplinary perspectives, it is a valuable resource for researchers and practitioners in academia and industry.

Download or read book Leveraging Textual Information for Social Media News Categorization and Sentiment Analysis written by Mahmudul Hasan and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent explosion in the popularity of social media platforms has fundamentally altered how people think about professional and personal connections. Data from social networks are increasingly being used for various reasons, including election prediction, sentimental analysis, marketing, communication, business, and education. Sentiment analysis and news categorization are crucial tasks to understand human feelings and access the news without much effort. In this paper, we first apply supervised and unsupervised Machine Learning (ML) algorithms for news categorization. After this, we propose a blending ensemble algorithm that outperforms the classical ML algorithms. Then, we process both structured and unstructured data for sentiment analysis based on the polarity of the text using TextBlob, which builds upon on natural language toolkit for processing textual data. We investigate Support Vector Machine, k-nearest Neighbors, Decision Tree, AdaBoost, Logistic Regression, Stochastic Gradient Descent (SGD), Ridge Classifier (RC), and Naive Bayes as supervised ML algorithms and K-Means Clustering and Non-negative Matrix Factorization as unsupervised ML algorithms. After evaluating the ML algorithms, we preprocess the text and propose an ensemble blending SGDR classifiers that build upon SGD and RC. The performance of the proposed ensemble outperforms all the algorithms. It shows 98.12% accuracy and besides this, the performance of all the algorithms increased after applying the string preprocessing technique at a significant rate. The result also indicates that linear models are more familiar than the tree-based and nonlinear models for news categorization.

Download or read book Detecting Fake News on Social Media written by Kai Shu and published by Springer Nature. This book was released on 2022-05-31 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, social media has become increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This book, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information: http://dmml.asu.edu/dfn/

Download or read book Sentic Computing written by Erik Cambria and published by Springer Science & Business Media. This book was released on 2012-07-28 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.

Download or read book First International Conference on Sustainable Technologies for Computational Intelligence written by Ashish Kumar Luhach and published by Springer Nature. This book was released on 2019-11-01 with total page 847 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers high-quality papers presented at the First International Conference on Sustainable Technologies for Computational Intelligence (ICTSCI 2019), which was organized by Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan, India, on March 29–30, 2019. It covers emerging topics in computational intelligence and effective strategies for its implementation in engineering applications.

Download or read book Sentiment Analysis for Social Media written by Carlos A. Iglesias and published by . This book was released on 2020 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

Download or read book Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media written by Keikhosrokiani, Pantea and published by IGI Global. This book was released on 2022-02-18 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Opinion mining and text analytics are used widely across numerous disciplines and fields in today’s society to provide insight into people’s thoughts, feelings, and stances. This data is incredibly valuable and can be utilized for a range of purposes. As such, an in-depth look into how opinion mining and text analytics correlate with social media and literature is necessary to better understand audiences. The Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics applied to opinion mining and text analytics on literary works and social media. It also focuses on theories, methods, and approaches in which data analysis techniques can be used to analyze data to provide a meaningful pattern. Covering a wide range of topics such as sentiment analysis and stance detection, this publication is ideal for lecturers, researchers, academicians, practitioners, and students.

Download or read book Semantically Enhanced Topic Modeling and Its Applications in Social Media written by Lifan Guo and published by . This book was released on 2013 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: As we witness the prosperity of the social media in the past few years, and feel the explosion of "user-generated content" on the Internet, there is little question that we have entered an era of Big Data. Those social media sites, such as Facebook, LinkedIn, Quora and Twitter have been important sources for a wide spectrum of users. Mushrooming numbers of tasks, such as community detection, personalized message recommendation and sentiment analysis, have become more important under such scenario. While many researches wish to use standard text mining tools to understand social media data, the heterogeneity and restricted length of data, prevents them from directly applying those tools. Among those tools, topic modeling (Blei D. , 2003) (Hofmann, 2009) (Steyvers, 2007), a type of probabilistic and statistical model for discovering the abstract "topics" that occur in a collection of documents, draw a lot of interests in recent ten years. Topic model can uncover the hidden structure in document collections and help us develop new ways to search, browse and summarize large archives of texts. Directly applying topic model to social media data, however, is not straightforward for the following reasons: (1) social-media data are essentially unstructured and include heterogeneous data types, such as text, clicks, votes and so on, while traditional topic model are used to analyze structured data, like archives of books, journals, and newspapers; (2) compared to focus on discovering topics, the purpose of using social media data is more complex, such as reliable information detection, sentiment detection, and recommendation. In other words, discovered topics are just intermediate results for further use; (3) traditional topic modeling technology assumes that words in documents are drawn independently from a set of topics and documents are identically distrusted in the corpus. Such an independently and identically distributed (i.i.d.) assumption, however, often does not hold in reality. Further, the i.i.d. assumption ignores semantic information existed on web. Therefore, it is reasonable to incorporate the existing knowledge into current unsupervised topic modeling in the purpose of semantically enhancing topic modeling technology. To address the facing challenges, this dissertation first proposes a semantically enhanced topic modeling framework that does not rely on independently and identically distributed (i.i.d.) assumption through utilizing existing knowledge. Experiments show that this framework enhanced current topic models since they are able to employ the relations of words to achieve better results compared to other traditional topic modeling methods. Second, we extend the framework into social media data targeting two research questions: 1) How to detect reliable authority and content information in community question answering? 2) How to enhance recommender system with items' reviews in communities. To answer the first question, we effectively extend LDA model to model the question and answers from different topic distribution in community question answering through semantic correlations between terms. Our model outperforms the model that directly apply LDA model to the same question and the model without enhanced semantic correlations. Also our model can utilize the topical information from questions, answers, questioner and answerer, in the purpose of detecting domain authority and reliable contents. Last but not least, we apply our model to recommender system. We propose an innovative concept, namely Item Social Reputation (ISR), to enhance current recommender system. Our model is to add another social dimension to items, in the purpose of effectively improving conversion rate of items recommendations. Furthermore, we can automatically determine the number of ISR of a certain item. Our experiments outperform the-state-of-the-art algorithms in the domain of sentiment analysis. Besides, our model shows potentials to be used to design a new interface of recommender systems.

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book The International Conference on Advanced Machine Learning Technologies and Applications AMLTA2018 written by Aboul Ella Hassanien and published by Springer. This book was released on 2018-01-26 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic, security, and intelligence swarms and optimization.

Download or read book Multiple Classifier Systems written by Fabio Roli and published by Springer Science & Business Media. This book was released on 2004-06 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Multiple Classifier Systems, MCS 2004, held in Cagliari, Italy in June 2004. The 35 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on bagging and boosting, combination methods, design methods, performance analysis, and applications.

Download or read book Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition written by Lior Rokach and published by World Scientific. This book was released on 2019-02-27 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.