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Numerical algorithms for personalized search in self-organizing information networks
This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data.
Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections.
Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.
eBook
A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis
The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F 1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.
Journal Article
Multilingual text analysis : challenges, models, and approaches
Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge - facts, rules, and relationships - that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains.
Book
Possible worlds in humanities, arts and sciences : proceedings of Nobel Symposium 65
No detailed description available for \"Possible Worlds in Humanities, Arts and Sciences\".
eBook
Understanding Thematic Analysis and the Debates Involving Its Use
The misconceptions researchers have about thematic analysis lead to various problems, which include publishing papers without mentioning the techniques they used to analyze their data. One reason such problems occur is that thematic analysis has been a poorly demarcated method for many years. Another has to do with the lack of literature on how this method differs from other approaches to research. In this paper, I aim to close this gap by explaining how different versions of thematic analysis vary from each other and discussing the controversies associated with each version. My conclusions are based on an analysis of what leading authors have published about this topic. I used a purposeful sample consisting of publications written by notable authors. I then analyzed this content to write a conceptual paper designed to enhance the understanding of different versions of thematic analysis and to document the controversies associated with each type.
Journal Article
Numerical algorithms for personalized search in self-organizing information networks
\"This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks.\" The book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding web-scale data.--[book cover]
Book
Rhetoric and Experience Architecture
Organizations value insights from reflexive, iterative processes of designing interactive environments that reflect user experience. \"I really like this definition of experience architecture, which requires that we understand ecosystems of activity, rather than simply considering single-task scenarios.\"—Donald Norman (The Design of Everyday Things)
eBook