NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination

Zhengxin Zhang, Qimin Zhou, Hao Wu
<span title="">2018</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aa3ggf3tbjarfj2zylzuxxqvdi" style="color: black;">Proceedings of The 12th International Workshop on Semantic Evaluation</a> </i> &nbsp;
In this paper, we put forward a system that competed at SemEval-2018 Task 1: "Affect in Tweets". Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation
more &raquo; ... oefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/s18-1015">doi:10.18653/v1/s18-1015</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/semeval/ZhangZW18.html">dblp:conf/semeval/ZhangZW18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4vuyo6fgtvdq3d4origrm5vo3e">fatcat:4vuyo6fgtvdq3d4origrm5vo3e</a> </span>
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