Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you'll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python's scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You'll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning. Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction. This audiobook includes:An introduction to machine learningExtracting structure from dataDeep learning and neural networksHow recommendation engines work Knowledge of Python is assumed for the listener. Douglas McIlwraith is a machine learning expert and data science practitioner in the field of Online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Table of Contents:1. Building applications for the intelligent web 2. Extracting structure from data: clustering and transforming your data 3. Recommending relevant content 4. Classification: placing things where they belong 5. Case study: click prediction for Online advertising 6. Deep learning and neural networks 7. Making the right choice 8. The future of the intelligent web li 1. Language: English. Narrator: Mark Thomas. Audio sample: http://samples.audible.de/bk/acx0/130626/bk_acx0_130626_sample.mp3. Digital audiobook in aax.
The goal of this work is to classify short Twitter messages with respect to their sentiment using data mining techniques. Twitter messages, or tweets, are limited to 140 characters. This limitation makes it more difficult for people to express their sentiment and as a consequence, the classification of the sentiment will be more difficult as well. The sentiment can refer to two different types: emotions and opinions. This research is solely focused on the sentiment of opinions. These opinions can be divided into three classes: positive, neutral and negative. The tweets are then classified with an algorithm to one of those three classes. Known supervised learning algorithms as support vector machines and naive Bayes are used to create a prediction model. Before the prediction model can be created, the data has to be pre-processed from text to a fixed-length feature vector. The features consist of sentiment-words and frequently occurring words that are predictive for the sentiment. The learned model is then applied to a test set to validate the model.
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The Social Web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present book deals with the problem of inferring information or patterns in general about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.
This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives, they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.
Social network analysis is a wide field in which researchers focus on the creation of models which explain social phenomenons like user behaviour, the prediction or detection of trending topics in the world wide web, social media and many others environments. Since research is bound to the availability of relevant data sets which are used for evaluation, there are mainly two kinds of scenarios. As online content by its very nature can generally be obtained by crawling techniques or created by the maintainer of the website by querying the respective database oranalysing log-files, a lot of scenarios capture the state of online social networks or represent the linking structure for parts of the internet. The second source of data which is recently getting increasingly more attention with the growing availability of sensors in mobile phones is the digital representation of user (inter-) actions in the real world. Those latter scenarios are often attributed as offline.In this thesis, we answer questions in the field of link prediction and information diffusion and evaluate our solutions in form of models for online and offline scenarios.The first problem is defined as the task to predict which edges in a temporally evolving network will be present in the future based on the information in the past. Some frequently used evaluation settings are the linking structure between web sites, the mention-relation in Twitter, the friendship relation in social networks or the citation and co-author network of a scientific community. We focus on user interactions in online social networks. The second of both fields tackles the task to predict the spread of knowledge in a social network. Those models are often used in order to predict the potential of viral marketing strategies which explicitly consider the mechanisms of wordof-mouth. We evaluate our research in the context of online and offline social networks.
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.
Big data is becoming more prevalent in psychology and the behavioral sciences, and so are the methodological and statistical issues that arise from its use. Psychologists need to be equipped to deal with these. Big data can be generated in experimental studies where, for example, participants' physiological and psychological responses are tracked over time or where human brain imaging is employed. Observational data from websites such as Facebook, Twitter, and Google is also of increasing interest to psychologists. These sometimes huge data sets, which are often too large for standard computers and can also contain multiple types of data, bring with them challenging questions about data quality and the generalizability of the results as well as which statistical tools are suitable for analyzing them.The contributions in this volume explore these challenges, looking at the potential of applying machine learning techniques to big data in psychology as well as the split/analyze/meta-analyze (SAM) approach, which allows big data to be split up into smaller datasets so they can be analyzed with conventional multivariate techniques on standard computers. The issues of replicability, prediction accuracy, and combining types of data are also investigated.
This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis.The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book's novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments, stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit, evaluation of HGFRNNs with different types of recurrent units, and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on 'social brokers' are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.