Affective State Prediction of Contextualized Concepts

Minglei Li, Qin Lu, Yunfei Long, Lin Gui
Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing, PMLR 66:45-57, 2017.

Abstract

Most studies on affective analysis of text focus on the sentiment or emotion expressed by a whole sentence or document. In this paper,we propose a novel approach to predict the affective states of a described event through the predictions of the corresponding subject, action and object involved in the described event. Rather than using a sentiment label or discrete emotion categories, the affective state is represented using the three dimensional evaluation-potency-activity(EPA) model. The main idea is to use automatically obtained word embedding as word representation and to use the Long Short-Term Memory(LSTM) network as the prediction model. Compared to the linear model used in the Affective Control Theory which uses manually annotated EPA lexicon, our proposed LSTM learning method using word embedding outperforms the linear model and word embedding also performs better than EPA lexicon. Most importantly, our work shows that automatically obtained word embedding outperforms manually constructed affective lexicons.

Cite this Paper

BibTeX
@InProceedings{pmlr-v66-li17a, title = {Affective State Prediction of Contextualized Concepts}, author = {Li, Minglei and Lu, Qin and Long, Yunfei and Gui, Lin}, booktitle = {Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing}, pages = {45--57}, year = {2017}, editor = {Lawrence, Neil and Reid, Mark}, volume = {66}, series = {Proceedings of Machine Learning Research}, month = {20 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v66/li17a/li17a.pdf}, url = {https://proceedings.mlr.press/v66/li17a.html}, abstract = {Most studies on affective analysis of text focus on the sentiment or emotion expressed by a whole sentence or document. In this paper,we propose a novel approach to predict the affective states of a described event through the predictions of the corresponding subject, action and object involved in the described event. Rather than using a sentiment label or discrete emotion categories, the affective state is represented using the three dimensional evaluation-potency-activity(EPA) model. The main idea is to use automatically obtained word embedding as word representation and to use the Long Short-Term Memory(LSTM) network as the prediction model. Compared to the linear model used in the Affective Control Theory which uses manually annotated EPA lexicon, our proposed LSTM learning method using word embedding outperforms the linear model and word embedding also performs better than EPA lexicon. Most importantly, our work shows that automatically obtained word embedding outperforms manually constructed affective lexicons.} }
Endnote
%0 Conference Paper %T Affective State Prediction of Contextualized Concepts %A Minglei Li %A Qin Lu %A Yunfei Long %A Lin Gui %B Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing %C Proceedings of Machine Learning Research %D 2017 %E Neil Lawrence %E Mark Reid %F pmlr-v66-li17a %I PMLR %P 45--57 %U https://proceedings.mlr.press/v66/li17a.html %V 66 %X Most studies on affective analysis of text focus on the sentiment or emotion expressed by a whole sentence or document. In this paper,we propose a novel approach to predict the affective states of a described event through the predictions of the corresponding subject, action and object involved in the described event. Rather than using a sentiment label or discrete emotion categories, the affective state is represented using the three dimensional evaluation-potency-activity(EPA) model. The main idea is to use automatically obtained word embedding as word representation and to use the Long Short-Term Memory(LSTM) network as the prediction model. Compared to the linear model used in the Affective Control Theory which uses manually annotated EPA lexicon, our proposed LSTM learning method using word embedding outperforms the linear model and word embedding also performs better than EPA lexicon. Most importantly, our work shows that automatically obtained word embedding outperforms manually constructed affective lexicons.
APA
Li, M., Lu, Q., Long, Y. & Gui, L.. (2017). Affective State Prediction of Contextualized Concepts. Proceedings of IJCAI 2017 Workshop on Artificial Intelligence in Affective Computing, in Proceedings of Machine Learning Research 66:45-57 Available from https://proceedings.mlr.press/v66/li17a.html.

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