Tweelyzer. An Approach to Sentiment Analysis of Tweets PDF Download

Author: Durgesh Samariya
Publisher: Anchor Academic Publishing
ISBN: 3960675909
Category : Computers
Languages : en
Pages : 78

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Book Description
The ongoing trend of people using microblogging to express their thoughts on various topics has increased the need for developing computerised techniques for automatic sentiment analysis on texts that do not exceed 200 characters. Twitter is a "micro-blogging" social networking site that has a large and rapidly growing base of users. Twitter's tweets or messages are limited to 140 characters. Because of this limitation, it is more difficult to express sentiment and the classification of the tweets is difficult as well. Sentiment analysis can be done on two types: emotion and opinion. This research completely focuses on sentiment analysis of opinions. These opinions can be divided in three different classes: positive, negative and neutral ( somewhere between positive and negative). The main goal of this study is to build a model that predicts election movement and provide sentiment score from Twitter messages (which can not exceed 140 characters). In this project, the author applies a novel approach that classifies sentiment and emotions of Twitter tweets automatically in positive, negative or neutral classes. For the sentiment, first of all, tweets from twitter were retrieved and converted into the dataset. After pre-processing the data the proposed algorithm named TWEELYZER was applied to the dataset. At the end, the performance of TWEELYZER was measured in terms of accuracy and recall. In this project, all tweets of people regarding to movies, brands, actors and actresses were collected from twitter and then cleaned and analysed according to the proposed algorithm. These tweets were collected using R Studio software. Several processes took place in pre-processing the tweets. After pre-processing the data, using R Studio led to several insights.

Author: Dr. Gaurav Gupta
Publisher: BookRix
ISBN: 3743852535
Category : Technology & Engineering
Languages : en
Pages : 76

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Book Description
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.

Author: C.H. Chen
Publisher: Springer
ISBN:
Category : Psychology
Languages : en
Pages : 676

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Book Description
Both pattern recognition and signal processing are rapidly growing areas. Organized with emphasis on many inter-relations between the two areas, a NATO Advanced Study Institute on Pattern Recognition and Signal Processing was held June 25th - July 4, 1978 at the E.N.S.T. (Department of Electronics) in Paris, France. This volume is the Proceedings of the Institute. It contains what I believed to be a truly outstanding collection of papers which cover all major activities in both pattern recognition and signal processing. The papers are grouped by topics as follows: I. Syntactic Methods: paper numbers 1, 2. II. Statistical Methods: paper numbers 3, 4, 5, 6. III. Detection and Estimation: paper numbers 7, 8. IV. Image Processing, Modelling, and Analysis: paper numbers 9, 10, 11, 12. V. Speech Application: paper numbers 13, 14. VI. Radar Application: paper number 15. Seismic Application: paper number 16. VII. Biomedical Application: paper numbers 17, 18, 19. VIII. IX. Reconstruction From Projections: paper numbers 20, 21- X. Signal Modelling and Application: paper numbers 22, 23, 24. XI. NATO Pattern Recognition Research Study Group Report: paper number 25. It is my strong belief that there is a need for continuing interaction between pattern recognition and signal processing. The book will serve as a useful text and reference for such a need, and for both areas. Finally on behalf of all participants of the Institute, I would like to thank Drs. T. Kester and M. N. Czdas of NATO for their support.

Author: H. Saif
Publisher: IOS Press
ISBN: 1614997519
Category : Computers
Languages : en
Pages : 310

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Book Description
Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people’s opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis on social media, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment. However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. In order to address this problem, the author investigates the role of word semantics in sentiment analysis of microblogs. Specifically, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, the author proposes several approaches in this book for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words’ co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider word semantics for sentiment analysis at both entity and tweet levels, surpass non-semantic approaches in most evaluation scenarios. This book will be of interest to students, researchers and practitioners in the semantic sentiment analysis field.

Author: Sudhir Pathak
Publisher:
ISBN: 9781726850209
Category :
Languages : en
Pages : 124

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Book Description
'Sentiment' literally means 'Emotions'. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analysis of text data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. With the rise of users posting their viewpoints in microblogging sites, sentiment analysis of the posted texts has turned into a happening field of research, as it serves as a potential source for studying the opinions held by the commenters towards an entity.

Author: Natalie Friedrich
Publisher: GRIN Verlag
ISBN: 3668112703
Category : Business & Economics
Languages : en
Pages : 72

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Book Description
Bachelor Thesis from the year 2015 in the subject Business economics - Information Management, grade: 1,3, University of Dusseldorf "Heinrich Heine" (Institut für Sprache und Information), language: English, abstract: This work analyzes tweets linking to scientific papers to find out if the tweets are positive, or negative or do not express an opinion. This will inform the meaning of tweets as a measure of impact in the context of altmetrics. The following research questions are examined: - In how far can sentiment analysis be used to detect positive or negative statements towards scientific papers expressed on Twitter? - Do tweets linking to scientific papers express positive or negative opinions? How do sentiments differ by academic discipline? - How do results affect the meaning of tweets to scientific papers as an altmetric indicator?

Author: Shamanth Kumar
Publisher: Springer Science & Business Media
ISBN: 1461493722
Category : Computers
Languages : en
Pages : 85

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Book Description
This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter’s APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.

Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668463040
Category : Computers
Languages : en
Pages : 1980

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Book Description
The rise of internet and social media usage in the past couple of decades has presented a very useful tool for many different industries and fields to utilize. With much of the world’s population writing their opinions on various products and services in public online forums, industries can collect this data through various computational tools and methods. These tools and methods, however, are still being perfected in both collection and implementation. Sentiment analysis can be used for many different industries and for many different purposes, which could better business performance and even society. The Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines discusses the tools, methodologies, applications, and implementation of sentiment analysis across various disciplines and industries such as the pharmaceutical industry, government, and the tourism industry. It further presents emerging technologies and developments within the field of sentiment analysis and opinion mining. Covering topics such as electronic word of mouth (eWOM), public security, and user similarity, this major reference work is a comprehensive resource for computer scientists, IT professionals, AI scientists, business leaders and managers, marketers, advertising agencies, public administrators, government officials, university administrators, libraries, students and faculty of higher education, researchers, and academicians.

Author: Carlos A. Iglesias
Publisher: MDPI
ISBN: 3039285726
Category : Technology & Engineering
Languages : en
Pages : 152

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Book Description
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.

Author: Erik Cambria
Publisher: Springer
ISBN: 3319553941
Category : Medical
Languages : en
Pages : 199

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Book Description
Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers’ sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well.