Capturing Global Mood Levels using Blog Posts

By Gilad Mishne, Maarten de Rijke. 2006

203 citations recorded by the source corpus.

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Citations in the corpus

Widespread Worry and the Stock Market (2010, methodology): “Blog posts and blog sentiment have been shown to predict product sales (Gruhl, et al. 2005; Mishne and Glance 2006) and to correspond to certain high profile events (Balog, Mishne and de Rijke 2006).”
A survey on sentiment detection of reviews (2009, methodology): “The correlation coefficient indicates how accurate the sentiment classification is, showing to what degree the fluctuation patterns (e.g., detection of peaks and drops) of a sentiment are predicted by the model ( Mishne & de Rijke, 2006 ).”
Beyond Sentiment: The Manifold of Human Emotions (2012, background): “[9] classified time stamped documents in order to show the cha nges in public moods over time.”
Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena (2009, background): “Personal websites such as blogs and online journals are often awash with emotive information and have been extensively used to deduct everyday happiness [12], explore trends and seasonality [1], forecast mood [13], and predict sales of books [7] and movies [14].”
A Tensor-Based Information Framework for Predicting the Stock Market (2016, background): “Previous studies have captured public moods from user activities [Szabo and Huberman 2010] and online content [Mishne and De Rijke 2006; Li et al. 2014b].”
Opinion Mining and Sentiment Analysis (2008, background): “Clearly, one can create a sentiment timeline simply by plotting the value of a chosen summary statistic at different times; the chosen statistic can reflect the prevailing polarity [170, 297] or simply the number of mentions, in which case what is being measured is perhaps not so much public opinion”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, background): “Examples of social emotion detection can be found in Mishne and De Rijke [2006] and Lei et al. [2014].”
Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter (2011, result): “Our work also naturally builds on and shows consistency with earlier work on blogs [23,39–44], which in recent years have subsided due the ascent of Twitter and other services such as Facebook.”
EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks (2017, background): “(Mishne and De Rijke, 2006) collect user-labeled mood from blog post text on LiveJournal and exploit them for predicting the intensity of moods over a time span rather than at the post level.”