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Abstract: Using a total of 20 million mood-annotated blog posts harvested between June 2005 and March 2006, we provide a time series analysis of the number of blog posts annotated with a mood. State-space methods are used to determine decompositions of the time series data associated with bloggers ' moods (either individual or aggegrated), allowing us to look for patterns of trend, seasonality and cycle. (Update)
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BibTeX entry: (Update)
@misc{ balog-decomposing,
author = "Krisztian Balog and Maarten de Rijke",
title = "Decomposing Bloggers' Moods - Towards a Time Series Analysis of Moods in
the Blogosphere",
url = "citeseer.ist.psu.edu/balog06decomposing.html" }
Citations (may not include all citations):650 Numerical methods for unconstrained optimization and nonline.. (context) - Dennis, Schnabel - 1983
48 Introduction to Time Series and Forecasting (context) - Brockwell, Davis - 2003
11 Time Series Analysis by State Space Methods (context) - Durbin, Koopman - 2001
4 Analysis and Modeling of Seasonal Time Series (context) - Box, Hillmer et al. - 1978
1 Identifying and explaining spikes in blog mood levels (context) - Balog, Mishne et al. - 2006
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