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        <title>DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method</title>
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            <namePart type="given">Alexandra</namePart>
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            <namePart type="given">Saif</namePart>
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    <abstract>In this paper, we present a novel ensemble learning architecture for emotion intensity analysis, particularly a novel framework of ensemble method. The ensemble method has two stages and each stage includes several single machine learning models. In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation. In stage2, we use two regression models including linear regression and XGBoost. The result of stage1 serves as the input of stage2, so the two different type models (linear and non-linear) in stage2 can describe the input in two opposite aspects. We also added a method for analyzing and splitting multi-words hashtags and appending them to the emotion intensity corpus before feeding it to our model. Our model achieves 0.571 Pearson-measure for the average of four emotions.</abstract>
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%0 Conference Proceedings
%T DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method
%A Jiang, Song
%A Han, Xiaotian
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F jiang-han-2017-dmgroup
%X In this paper, we present a novel ensemble learning architecture for emotion intensity analysis, particularly a novel framework of ensemble method. The ensemble method has two stages and each stage includes several single machine learning models. In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation. In stage2, we use two regression models including linear regression and XGBoost. The result of stage1 serves as the input of stage2, so the two different type models (linear and non-linear) in stage2 can describe the input in two opposite aspects. We also added a method for analyzing and splitting multi-words hashtags and appending them to the emotion intensity corpus before feeding it to our model. Our model achieves 0.571 Pearson-measure for the average of four emotions.
%R 10.18653/v1/W17-5234
%U https://aclanthology.org/W17-5234
%U https://doi.org/10.18653/v1/W17-5234
%P 243-248

Markdown (Informal)

[DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method](https://aclanthology.org/W17-5234) (Jiang & Han, WASSA 2017)

• DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method (Jiang & Han, WASSA 2017)

ACL

• Song Jiang and Xiaotian Han. 2017. DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 243–248, Copenhagen, Denmark. Association for Computational Linguistics.