Paper Info
Title
EmotiBlog: a finer-grained and more precise learning of subjectivity expression models
Abstract
The exponential growth of the subjective information in the framework of the Web 2.0 has led to the need to create Natural Language Processing tools able to analyse and process such data for multiple practical applications. They require training on specifically annotated corpora, whose level of detail must be fine enough to capture the phenomena involved. This paper presents EmotiBlog -- a finegrained annotation scheme for subjectivity. We show the manner in which it is built and demonstrate the benefits it brings to the systems using it for training, through the experiments we carried out on opinion mining and emotion detection. We employ corpora of different textual genres -- a set of annotated reported speech extracted from news articles, the set of news titles annotated with polarity and emotion from the SemEval 2007 (Task 14) and ISEAR, a corpus of real-life self-expressed emotion. We also show how the model built from the EmotiBlog annotations can be enhanced with external resources. The results demonstrate that EmotiBlog, through its structure and annotation paradigm, offers high quality training data for systems dealing both with opinion mining, as well as emotion detection.
Year
Venue
Keywords
2010
Linguistic Annotation Workshop
real-life self-expressed emotion,high quality training data,annotated corpus,opinion mining,annotation paradigm,annotated reported speech,emotiblog annotation,news article,subjectivity expression model,precise learning,finegrained annotation scheme,emotion detection
Field
DocType
Citations 
Training set,Annotation,SemEval,Information retrieval,Sentiment analysis,Subjectivity,Level of detail,Computer science,Emotion detection,Natural language processing,Artificial intelligence
Conference
11
PageRank 
References 
Authors
0.76
23
4
Name
Order
Citations
PageRank
Ester Boldrini19412.69
Alexandra Balahur259340.19
Patricio Martínez-Barco328243.37
Andrés Montoyo467867.78