.png){kind=icon}
.png){kind=icon}
Return
Implicit mood computing via LSTM and semantic mapping
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-020-04909-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article proposes an implicit mood computing system. The implicit mood computing task is a part of affective computing. Previous works in affective computing mostly focus on twitters, blogs, movie interviews, and news corpus. These works detect sentiment polarity (positive/negative), emotion types (joy, sadness, anger, etc.), or mood types (boring, tired, happy, etc.) of the text. Different from previous studies, our work focuses on the literature texts and detects the implicit mood of them. The implicit mood is sometimes discussed as the tone or the atmosphere of the text. The implicit mood is an important affective feature in the literature such as poetry, prose, and drama. Our work regards the implicit mood as a semantic phenomenon. We capture the feature of implicit mood via a semantic mapping approach and the long short-term memory neural network. The proposed system is capable of identifying 12 kinds of implicit moods with a promising result.
Keywords:
Affective computing
Mood
LSTM
Semantic mapping