Annotating and Identifying Emotions in Text Page: 21
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Annotating and Identifying Emotions in Text
Carlo Strapparava and Rada Mihalcea
Abstract. This paper focuses on the classification of emotions and polarity in news
headlines and it is meant as an exploration of the connection between emotions and
lexical semantics. We first describe the construction of the data set used in eval-
uation exercise "Affective Text" task at SEMEVAL 2007, annotated for six basic
emotions: ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and for POSI-
TIVE and NEGATIVE polarity. We also briefly describe the participating systems and
their results. Second, exploiting the same data set, we propose and evaluate sev-
eral knowledge-based and corpus-based methods for the automatic identification of
emotions in text.
Emotions have been widely studied in psychology and behavior sciences, as they
are an important element of human nature. They have also attracted the attention of
researchers in computer science, especially in the field of human computer interac-
tion, where studies have been carried out on facial expressions (e.g., ) or on the
recognition of emotions through a variety of sensors (e.g., ).
Although only relatively little work has been carried out so far on the auto-
matic identification of emotions in text [31, 1], the automatic detection of emo-
tions in texts is becoming increasingly important from an applicative point of
view. Consider for example the tasks of opinion mining and market analysis, affec-
tive computing, or natural language interfaces such as e-learning environments or
e-mail: s trappa@fbk. edu
University of North Texas
e-mail: rada@cs . unt . edu
G. Armano et al. (Eds.): Intelligent Information Access, SCI 301, pp. 21-38.
springerlink.com ( Springer-Verlag Berlin Heidelberg 2010
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Strapparava, Carlo, 1962- & Mihalcea, Rada, 1974-. Annotating and Identifying Emotions in Text, chapter, 2010; [Berlin, Germany]. (digital.library.unt.edu/ark:/67531/metadc31010/m1/1/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.