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Sentiment Analysis in Social Networks

. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an;

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Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book;

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Sentiment Analysis and Opinion Mining

due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social;

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From Social Data Mining and Analysis to Prediction and Community Detection

of online social networks, it focuses on important and upcoming topics of social network analysis and mining such as the latest in sentiment trends;

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Sentiment Analysis and Opinion Mining

due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social;

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Sentiment Analysis for Social Media

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Computational Collective Intelligence

submissions. They are organized in topical sections such as multi-agent systems; social networks and NLP; sentiment analysis; computational;

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Computational Collective Intelligence

submissions. They are organized in topical sections such as multi-agent systems; social networks and NLP; sentiment analysis; computational;

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Web Information Systems Engineering - WISE 2016

Detection; Data Mining; Sentiment Analysis; Ranking in Social Networks; Microblog Data Analysis; Query Processing; Spatial and Temporal Data; Graph;

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Web Information Systems Engineering - WISE 2016

Detection; Data Mining; Sentiment Analysis; Ranking in Social Networks; Microblog Data Analysis; Query Processing; Spatial and Temporal Data; Graph;

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Sentiment Analysis and Knowledge Discovery in Contemporary Business

online collaborative media. However, conducting sentiment analysis on these platforms can be challenging, especially for business professionals;

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Innovative Data Communication Technologies and Application

, performance analysis, data validation and verification models, machine learning, sentiment analysis, and various data analysis techniques.;

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Trends in Applied Knowledge-Based Systems and Data Science

; natural language processing and sentiment analysis; semantic Web and social networks; computer vision; medical diagnosis system and bio;

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Sentiment Analysis

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem;

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Online Social Networks

communication data generated by social media is revolutionizing the field of social networks analysis, the text discusses the use of large- scale;

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Subjective Well-Being and Social Media

Deals with sample bias and selection bias in social media Reconciles official statistics and big data Language independent sentiment;

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R Deep Learning Projects

-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning;

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Dynamic Social Network Modeling and Analysis

insights, commentary, and critiques on the developing body of social network analysis research and application. The secondary purpose was to;

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Advances in Knowledge Discovery and Data Mining

learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining;

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Advances in Knowledge Discovery and Data Mining

learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining;

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The Oxford Handbook of Social Networks

, political, and social domains can hardly be disputed. Social networks fundamentally shape our lives and social network analysis has become a vibrant;

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Algebraic Models for Social Networks

As the analysis of social networks, or networks of interpersonal and social relationships among social groups, has become an increasingly;

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Structural Analysis in the Social Sciences

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and;

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Structural Analysis in the Social Sciences

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and;

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Multimodal Sentiment Analysis

extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment;

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Politics and Big Data

analysis to improve upon them. The authors carry out a meta-analysis of the existing literature to show the conditions under which social media;

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Politics and Big Data

analysis to improve upon them. The authors carry out a meta-analysis of the existing literature to show the conditions under which social media;

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