|Title||AM: textual attitude analysis model|
|Publication Type||Conference Paper|
|Year of Publication||2010|
|Authors||Neviarouskaya, A, Prendinger, H, Ishizuka, M|
|Conference Name||NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text|
|Publisher||Association for Computational Linguistics|
|Conference Location||Los Angeles, California|
The automatic analysis and classification of text using fine-grained attitude labels is the main task we address in our research. The developed @AM system relies on compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on fine-grained level was 62%, on middle level - 71%, and on top level - 88%.