Expiriments with mood classification in blog posts

By Gilad Mishne. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/14e0fe6592de59ed3bb4f0ae31ea6ed153273443

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Citations in the corpus

Learning to identify emotions in text (2008, methodology): “This corpus is a subset of the corpus used in the experiments reported in [10].”
Sentiment Analysis: Adjectives and Adverbs are Better than Adjectives Alone (2007, background): “Some preliminary studies, such as in [1], classify moods of blog text using a large collection of blog posts containing the authors indication of their state of mind at the time of writing: whether the author was depressed, cheerful, bored, and so on.”
Beyond Sentiment: The Manifold of Human Emotions (2012, methodology): “Mishne (2005) and G´en´ereux and Evans (2006) examine a similar analysis task using blog posts with standard machine learning techniques, while Keshtkar and Inkpen (2009) exploit a mood hierarchy to improve classiﬁcation results.”
A Study of Information Retrieval Weighting Schemes for Sentiment Analysis (2010, unknown): “, 2006), news (Devitt and Ahmad, 2007) and blogs (Ounis et al., 2008; Mishne, 2005).”
Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation (2006, background): “All these studies involve data sets gathered with specific purposes in a well-defined en vironment: researchers discussing the time and venue of a meeting [16], bloggers lab elling with their moods blogs posted on a Web site [14], participants of e-negotiati ns held by a negotiation support system [”
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys (2010, methodology): “Mishne (2005) used an ontology of over 100 moods assigned to blog posts to classify blog texts according to moods.”
An unobtrusive behavioral model of "gross national happiness" (2010, methodology): “This top-down approach is useful for the study of known topics, as it allows for validation studies to cross word-use contexts and corpora: A more bottom-up approach, such as LSA, may allow discovery of positive terms not present in the LIWC corpus [12], but would require separate validation of the ”
Twitter Sentiment Classiﬁcation using Distant Supervision (2009, background): “Previous research on analyzing blog posts includes [6].”
A machine learning approach to sentiment analysis in multilingual Web texts (2009, background): “Opinion mining in legal blogs has been performed (Conrad and Schilder 2007), mood levels were detected in blog posts (Mishne 2005; Mishne and de Rijke 2006), six basic emotion categories were recognized in blog sentences (Aman and Szpakowicz 2008), but these studies did rarely involve the processing”
Fusing audio, visual and textual clues for sentiment analysis from multimodal content (2016, unknown): “So far, approaches to text-based emotion and sentiment detection rely mainly on rule-based techniques, bag of words modeling using a large sentiment or emotion lexicon [38], or statistical approaches that assume the availability of a large dataset annotated with polarity or emotion labels [39].”
Harnessing Twitter "Big Data" for Automatic Emotion Identification (2012, background): “Part-of-Speech (POS): POS features have been proven effective in sentiment/emotion classification [3], [8].”
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text (2007, unknown): “Recent work in AI explores methods for the automatic detection of other types of pragmatic variation in text and conversation, such as emotion (Oudeyer, 2002; Liscombe, Venditti, & Hirschberg, 2003), deception (Newman, Pennebaker, Berry, & Richards, 2003; Enos, Benus, Cautin, Graciarena, Hirschberg,”
Gender Classification of Weblog Authors (2006, background): “In particular, researchers have attempted to mine writing moods, product opinions, and underlying social networks from blogs (Gruhl et al. 2004; Liu, Hu, & Cheng 2005; Mishne 2005).”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, background): “Mishne [2005] LiveJournal 40 moods No Support Vector Machine”
Affective News: The Automated Coding of Sentiment in Political Texts (2012, background): “For examples in computational linguistics, see, for instance, Généreux and Evans (2006); Hatzivassiloglou and McKeown (1997); Joachims (1998); Kim and Hovy (2006); Kushal, Lawrence, and Pennock (2003); Leshed and Kaye (2006); Mishne (2005); Pang et al. (2002); and Wiebe (2000).”