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.

Human is a social animal, this line itself explains the importance of society in one's life. Society brings stability, a medium to express thoughts. Society leads to social interaction which eventually brings thoughtful minds. Humans have the intrinsic nature of analyzing and opinionating things and persons. This keen nature of humanity has emerged as a new field of analysis that is social data analysis. Internet has merged the world today and as a result, human social circles have expanded. There are various peculiar social networking sites available on the internet, some of them are on Facebook, Twitter, LinkedIn and many more. Each maintains accounts of billions of active users and the huge amount of data is being produced as a result of interactions over such sites. Hence analyzing this data is a tedious task. But analysis of such online social communities and predicting their behavior is of great importance for businesses and academics. For our research purpose, we have used Twitter as a key medium for social data. This study aims to develop a research-based application using twitter and R-tool for social data analysis.

Human is a social animal, this line itself explains the importance of society in one's life. Society brings stability, a medium to express thoughts. Society leads to social interaction which eventually brings thoughtful minds. Humans have the intrinsic nature of analyzing and opinionating things and persons. This keen nature of humanity has emerged as a new field of analysis that is social data analysis. Internet has merged the world today and as a result, human social circles have expanded. There are various peculiar social networking sites available on the internet, some of them are on Facebook, Twitter, LinkedIn and many more. Each maintains accounts of billions of active users and the huge amount of data is being produced as a result of interactions over such sites. Hence analyzing this data is a tedious task. But analysis of such online social communities and predicting their behavior is of great importance for businesses and academics. For our research purpose, we have used Twitter as a key medium for social data. This study aims to develop a research-based application using twitter and R-tool for social data analysis.

Erscheinungsdatum: 25.11.2013, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Twitter Data Analytics, Autor: Kumar, Shamanth // Morstatter, Fred // Liu, Huan, Verlag: Springer-Verlag GmbH // Springer US, Sprache: Englisch, Schlagworte: Roboter // Robotik // Industrieroboter // Bildbearbeitung // Bildverarbeitung // Grafik // EDV // Programmieren // Data Mining // Expertensystem // Wissensbasiertes System // Informatik // Schnittstelle // COMPUTERS // Databases // Grafische und digitale Media-Anwendungen // Datenbankprogrammierung // Wissensbasierte Systeme // Expertensysteme // Interfacedesign // Benutzerfreundlichkeit // Usability // Datenerfassung und // analyse, Rubrik: Informatik, Seiten: 77, Abbildungen: 26 illus., 26 schwarz-weiße Abbildungen, 1 schwarz-weiße Tabellen, Herkunft: GROSSBRITANNIEN (GB), Reihe: SpringerBriefs in Computer Science, Gewicht: 155 gr, Verkäufer: averdo

Tight competition among companies has been making many companies to increase their efforts to collect information from various media for the sake of their service analysis. Recorded data in social media such as Twitter can be used as a potential data to be analyzed, since Twitter is becoming one of the most popular social media in the world. Text classification is developed to observe positive and negative opinions of the customers on the services given by company in the form of heat map. Prior to developing the text classifier, data preprocessing was performed in order to determine attributes, to choose data accompanying with their associated coordinates, and to decide which words are considered as positive or negative opinion. Using coordinates of data that have been classified, a heat map was used to visualize density level gradation of positive and negative opinions. The use of heat map to determine customers' coordinate positions whose opinions have been classified will ease company to observe customers' opinions density level based on their color gradation visualization that are presented in certain radius from one coordinate observing point specified by the company.

Tight competition among companies has been making many companies to increase their efforts to collect information from various media for the sake of their service analysis. Recorded data in social media such as Twitter can be used as a potential data to be analyzed, since Twitter is becoming one of the most popular social media in the world. Text classification is developed to observe positive and negative opinions of the customers on the services given by company in the form of heat map. Prior to developing the text classifier, data preprocessing was performed in order to determine attributes, to choose data accompanying with their associated coordinates, and to decide which words are considered as positive or negative opinion. Using coordinates of data that have been classified, a heat map was used to visualize density level gradation of positive and negative opinions. The use of heat map to determine customers' coordinate positions whose opinions have been classified will ease company to observe customers' opinions density level based on their color gradation visualization that are presented in certain radius from one coordinate observing point specified by the company.

With microblogging platforms such as Twitter generating huge amounts of textual data every day, the possibilities of knowledge discovery through Twitter data becomes increasingly relevant. Similar to the public voting mechanismnon websites such as the Internet Movie Database (IMDb) that aggregates movies ratings, Twitter content contains reflections of public opinion about movies. This study aims to explore the use of Twitter content as textual data for predicting the movie rating. In this study, we extract number of tweets and compiled to predict the rating scores of newly released movies. Predictions were done with the algorithms, exploring the tweet polarity. In addition, this study explores the use of several different kinds of tweet classification Algorithm and movie rating algorithm. Results show that movie rating developed by our application is compared to IMDB and Rotten Tomatoes.

With microblogging platforms such as Twitter generating huge amounts of textual data every day, the possibilities of knowledge discovery through Twitter data becomes increasingly relevant. Similar to the public voting mechanismnon websites such as the Internet Movie Database (IMDb) that aggregates movies ratings, Twitter content contains reflections of public opinion about movies. This study aims to explore the use of Twitter content as textual data for predicting the movie rating. In this study, we extract number of tweets and compiled to predict the rating scores of newly released movies. Predictions were done with the algorithms, exploring the tweet polarity. In addition, this study explores the use of several different kinds of tweet classification Algorithm and movie rating algorithm. Results show that movie rating developed by our application is compared to IMDB and Rotten Tomatoes.

Any organization that is driven by the needs and wants of customers has to understand customer behavior from any media. Twitter is becoming one of the most popular social media in the world. Nowadays, many third-party applications of Twitter have been making customers freely to share their opinions anywhere and anytime. However, it is difficult to understand the change of customer behavior, because the tweet is posted in erratic likelihood. Therefore, visualizing the time pattern of customer emotion behavior on Twitter can play a crucial role in decision-making. Available data visualizing tools, such as D3.js, motivate us to develop and to explore time dimension of Twitter data in 2D visualization.