Seeing Stars of Valence and Arousal in Blog Posts

@article{Paltoglou2013SeeingSO,
  title={Seeing Stars of Valence and Arousal in Blog Posts},
  author={Georgios Paltoglou and Mike A Thelwall},
  journal={IEEE Transactions on Affective Computing},
  year={2013},
  volume={4},
  pages={116-123}
}
Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two… Expand
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References

SHOWING 1-10 OF 50 REFERENCES
Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media
TLDR
The results demonstrate that the proposed algorithm, even though unsupervised, outperforms machine learning solutions in the majority of cases, overall presenting a very robust and reliable solution for sentiment analysis of informal communication on the Web. Expand
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales
TLDR
A meta-algorithm is applied, based on a metric labeling formulation of the rating-inference problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. Expand
Sentiment in short strength detection informal text
TLDR
A new algorithm is partly filled with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Expand
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
TLDR
A novel machine-learning method is proposed that applies text-categorization techniques to just the subjective portions of the document, which greatly facilitates incorporation of cross-sentence contextual constraints. Expand
Sentiment in Twitter events
TLDR
A study of a month of English Twitter posts is reported, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely and using the top 30 events as a measure of relative increase in (general) term usage. Expand
A Study of Information Retrieval Weighting Schemes for Sentiment Analysis
TLDR
It is shown that variants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. Expand
Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization
TLDR
A graph-based semi-supervised learning algorithm is presented to address the sentiment analysis task of rating inference and achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training. Expand
Thumbs up? Sentiment Classification using Machine Learning Techniques
TLDR
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging. Expand
SemEval-2007 Task 14: Affective Text
TLDR
The data set used in the evaluation and the results obtained by the participating systems are described, meant as an exploration of the connection between emotions and lexical semantics. Expand
Learning Subjective Language
TLDR
This article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features. Expand
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