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首页> 外文期刊>Affective Computing, IEEE Transactions on >Emotion Recognition on Twitter: Comparative Study and Training a Unison Model
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Emotion Recognition on Twitter: Comparative Study and Training a Unison Model

机译：Twitter上的情感认同：比较研究和培训协调模型

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摘要

Despite recent successes of deep learning in many fields of natural language processing, previous studies of emotion recognition on Twitter mainly focused on the use of lexicons and simple classifiers on bag-of-words models. The central question of our study is whether we can improve their performance using deep learning. To this end, we exploit hashtags to create three large emotion-labeled data sets corresponding to different classifications of emotions. We then compare the performance of several word- and character-based recurrent and convolutional neural networks with the performance on bag-of-words and latent semantic indexing models. We also investigate the transferability of the final hidden state representations between different classifications of emotions, and whether it is possible to build a unison model for predicting all of them using a shared representation. We show that recurrent neural networks, especially character-based ones, can improve over bag-of-words and latent semantic indexing models. Although the transfer capabilities of these models are poor, the newly proposed training heuristic produces a unison model with performance comparable to that of the three single models.
机译：尽管最近在许多自然语言处理领域的深度学习取得了成功，但之前对Twitter的情感认可研究主要集中在词汇模型上使用词汇和简单的分类器。我们研究的核心问题是我们是否可以使用深度学习来提高他们的表现。为此，我们利用HashTags创建了与不同的情绪分类相对应的三个大情绪标记的数据集。然后，我们将几个基于词和字符的反型和卷积神经网络的性能进行了比较，具有袋式和潜在语义索引模型的性能。我们还研究了不同分类的情绪分类之间的最终隐藏状态表示的可转换性，以及是否有可能使用共享表示来构建用于预测所有这些的协调模型。我们表明经常性的神经网络，尤其是基于性质的神经网络，可以改善单词袋和潜在语义索引模型。虽然这些模型的转移能力很差，但是新建议的训练启发式态度产生了一个具有与三个单一模型的性能的统一模型。

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