Universal affective model for Readers’ emotion classification over short texts

作者：

Highlights：

• A novel universal affective model for classifying social emotions is proposed.

• ATF-IDF is developed to enhance the semantic relationships between biterms.

• A word-level emotional lexicon is established for background words by using SWAT.

• UAM is very effective in detecting emotions in both short texts and long texts.

摘要

•A novel universal affective model for classifying social emotions is proposed.•ATF-IDF is developed to enhance the semantic relationships between biterms.•A word-level emotional lexicon is established for background words by using SWAT.•UAM is very effective in detecting emotions in both short texts and long texts.

论文关键词：Topic model,Emotion classification,Biterm,Short text

论文评审过程：Received 2 May 2018, Revised 7 July 2018, Accepted 11 July 2018, Available online 26 July 2018, Version of Record 6 August 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.027