Download or read book Sentiment Analysis and Review Ranking Using Na ve Bayes with Weights in Online Social Networks written by Brandon Joyce and published by . This book was released on 2019 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Online reviews are critical in many aspects, for business as well as customers. Yet the accuracy and trustworthiness of these reviews are usually unsubstantiated and little research has been performed to investigate them. For the 2016 US Presidential election, many people expressed their likes or dislikes for a particular presidential candidate. Our aim was to calculate the sentiment expressed by these tweets, and then compare this sentiment with polling data to see how much correlation they share. We used a lexicon and Naive Bayes Machine Learning Algorithm to calculate the sentiment of political tweets collected one-hundred days before the election. We used manually labeled tweets as well as automatically labeled tweets based on hashtag content/topic. Our results suggest that Twitter is becoming a more reliable platform in comparison to previous work. Furthermore, we use a set of Yelp reviews on various topics (food, hotel, etc.) as an example to perform sentiment analysis and investigate the correlation between review comment sentiment and its numeric rating. We used feature selection techniques to statistically remove redundant words from reviews, thus improving run time and accuracy. Our method gives higher weight to those terms/words appearing in reviews with more useful votes. These techniques combined with Naive Bayes approach achieves an overall accuracy of 75%. More interestingly, our method is shown to perform well in 1-star and 5-star reviews, with 92% accuracy for the latter. With such a strong accuracy, we argue that the proposed sentiment analysis technique can be used to shed light on all online comments, especially those without numerical ratings."--Abstract from author supplied metadata

Download or read book SENTIMENT ANALYSIS OF ENGLISH TWEETS USING DATA MINING written by Dr. Gaurav Gupta and published by BookRix. This book was released on 2018-03-26 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the popularity of internet it becomes very easy for people to share their views over social networking websites. Most popular website among them is twitter. Twitter is a widely used social networking website that is used by the numerous people to give their opinion regarding a particular topic or product. So, today it becomes necessary to analyze the tweet of the people. The process to analyze and interpret the tweets is known as sentiment analysis. The main motive of this project is to identify how the tweets on the social networking website are used to identify the opinion of people regarding the particular product or policy. Twitter is a online website that allows the user to post the status of maximum 140 characters. Twitter has over 200 million registered users and 100 million active users [34]. So it comes to be a great source of valuable information. This project aims to develop a better way for sentiment analysis which is nothing a simple way to classify the tweets into positive, negative or neutral. The result of the sentiment analysis can be used by various organizations. Sentiment analysis can be used for forecasting the stock exchange, used to predict the popularity of any product in market, or used to predict the result of elections based on the public views on the social sites. The main motive of project is to develop a better way to accurately classify the unknown tweets according to their content.

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 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 Recent Findings in Intelligent Computing Techniques written by Pankaj Kumar Sa and published by Springer. This book was released on 2018-11-03 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three volume book contains the Proceedings of 5th International Conference on Advanced Computing, Networking and Informatics (ICACNI 2017). The book focuses on the recent advancement of the broad areas of advanced computing, networking and informatics. It also includes novel approaches devised by researchers from across the globe. This book brings together academic scientists, professors, research scholars and students to share and disseminate information on knowledge and scientific research works related to computing, networking, and informatics to discuss the practical challenges encountered and the solutions adopted. The book also promotes translation of basic research into applied investigation and convert applied investigation into practice.

Download or read book Speech Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sentiment Analysis in Social Networks written by Federico Alberto Pozzi and published by Morgan Kaufmann. This book was released on 2016-10-06 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics

Download or read book Smart Innovations in Communication and Computational Sciences written by Shailesh Tiwari and published by Springer. This book was released on 2018-11-19 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the latest advances and research findings in the fields of computational science and communication. The areas covered include smart innovation; systems and technologies; embedded knowledge and intelligence; innovation and sustainability; advanced computing; and networking and informatics. It also focuses on the knowledge-transfer methodologies and the innovation strategies employed to make these effective. This fascinating compilation appeals to researchers, academics and engineers around the globe.

Download or read book Twitter Sentiment Analysis Using N Gram With Naive Bayes Classifier written by Rajat Kumar Arya and published by . This book was released on 2020 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this current period, online life assumes a significant job in information trade, sharing their contemplation. Enthusiastic Effect of an individual keeps up a significant job on their everyday life. Assessment Analysis is a strategy of breaking down the feelings and extremity of considerations of the individual. Twitter is a primary stage on sharing the thought's, supposition and estimations on various events. Twitter Sentimental Analysis is strategy for investigating the feelings from tweets (message posted by client in twitter). Tweets are useful in extricating the Sentimental qualities from the client. The information give the Polarity sign like positive, negative or fair-minded qualities. It is centered around the individual's tweets and the hash labels for understanding the circumstances in every part of the rules. The paper is to investigate the celebrated individual's id's or hash labels for understanding the outlook of individuals in every circumstance when the individual has tweeted or has followed up on certain occurrences. The proposed framework is to break down the conclusion of the individuals utilizing python, twitter API, Unigrams + Bigrams + Trigrams, prepared on Naive Bayes Classifier. As the outcomes it serves to investigation the post with a superior precision.

Download or read book Sentiment Analysis and Opinion Mining written by Bing Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography

Download or read book Naive Bayes Algorithm for Twitter Sentiment Analysis and Its Implementation in MapReduce written by Zhaoyu Li and published by . This book was released on 2014 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data has been growing exponentially in recent years. With the development of information highway, data can be generated and collected very fast, and the data is so large that it has exceeded the limit of our conventional processing methods and applications. The social network is one of many data explosion areas. Among all social network medias, Twitter has become one of the most important platforms to share and communicate with friends. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document, and the sentiment analysis on Twitter has also been used as a valid indicator of stock prices in the past. Naive Bayes is an algorithm to perform sentiment analysis. MapReduce programming model provides a simple and powerful model to implement distributed applications without having deeper knowledge of parallel programming. When a new hypothetical MapReduce sentiment analysis system is built to provide certain performance goal, we are lack of the benchmark and the traditional trial-and-error solution is extremely time-consuming and costly. In this thesis we implemented a prototype system using Naive Bayes to find the correlation between the geographical sentiment on Twitter and the stock price behavior of companies. Also we implemented the Naive Bayes sentiment analysis algorithm in MapReduce model based on Hadoop, and evaluated the algorithm on large amount of Twitter data with different metrics. Based on the evaluation results, we provided a comprehensive MapReduce performance prediction model for Naive Bayes based sentiment analysis algorithm. The prediction model can predict task execution performance within a window, and can also be used by other MapReduce systems as a benchmark in order to improve the performance.

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 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 Applying Data Science and Learning Analytics Throughout a Learner s Lifespan written by Trajkovski, Goran and published by IGI Global. This book was released on 2022-05-06 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners’ journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner’s lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.

Download or read book Sentiment Analysis for Social Media written by Carlos A. Iglesias and published by MDPI. This book was released on 2020-04-02 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 Mining Text Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2012-02-03 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Download or read book Opinion Mining and Sentiment Analysis written by Bo Pang and published by Now Publishers Inc. This book was released on 2008 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems.