With the development of social network platforms, discussion forums, and question answering websites, a huge number of short messages that typically contain a few words for an individual document are posted by online users. In these short messages, emotions are frequently embedded for communicating opinions, expressing friendship, and promoting influence. It is quite valuable to detect emotions from short messages, but the corresponding task suffers from the sparsity of feature space. In this article, we first generate term groups co-occurring in the same context to enrich the number of features. Then, two basic supervised topic models are proposed to associate emotions with topics accurately. To reduce the time cost of parameter estimation, we further propose an accelerated algorithm for our basic models. Extensive evaluations using three short corpora validate the efficiency and effectiveness of the accelerated models for predicting the emotions of unlabeled documents, in addition to generate the topic-level emotion lexicons.
This work was supported in part by the National Natural Science Foundation of China under Grant 61972426, in part by the Interdisciplinary Research Scheme of the Dean’s Research Fund 2018-19 under Grant FLASS/DRF/IDS-3, in part by the Departmental Collaborative Research Fund 2019 under Grant MIT/DCRF-R2/18-19, in part by the Top-Up Fund (TFG-04) and Seed Fund (SFG-10) for General Research Fund/Early Career Scheme of the Dean’s Research Fund (DRF) 2018-19, in part by the General Research Fund Proposal under Grant RG 39/2019-2020R, in part by the Internal Research Grant of the Education University of Hong Kong under Grant RG 90/2018-2019R, and in part by the Collaborative Research Fund by the Research Grants Council of the Hong Kong under Project C1031-18G. The work of X. Wang was supported by the National Natural Science Foundation of China under Grant 61732011. This article is an extended journal version of a conference paper published at DASFAA 2017 .
- Accelerated algorithm
- emotion detection
- short text analysis
- topic model