论文标题
基于表情符号的细颗粒注意网络用于微博评论中的情绪分析
Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments
论文作者
论文摘要
微博已成为人们实时表达情绪的社交平台，从微博上的信息中分析用户情感倾向是一种趋势。表情符号的动态特征会影响微博文本的情感极性。由于现有模型很少考虑表情符号情感极性的多样性，因此本文提出了基于Albert-Faet的微博情感分类模型。我们通过Albert预训练模型获得文本嵌入，并通过基于注意力的LSTM网络学习emoji嵌入。此外，提出了一种细粒度的注意机制来捕获纯文本和表情符号之间的单词级相互作用。最后，我们将这些特征加入并将其馈入CNN分类器，以预测微博的情感标签。为了验证模型和细粒度注意网络的有效性，我们进行了比较实验和消融实验。比较实验表明，该模型在三个评估指标（准确性，精度和回忆）中的表现优于先前的方法，并且该模型可以显着改善情感分类。消融实验表明，与Albert-AET相比，所提出的模型Albert-Faet在指标中更好，表明细粒度的注意力网络可以理解表情符号的多元化信息。
Microblogs have become a social platform for people to express their emotions in real-time, and it is a trend to analyze user emotional tendencies from the information on Microblogs. The dynamic features of emojis can affect the sentiment polarity of microblog texts. Since existing models seldom consider the diversity of emoji sentiment polarity,the paper propose a microblog sentiment classification model based on ALBERT-FAET. We obtain text embedding via ALBERT pretraining model and learn the inter-emoji embedding with an attention-based LSTM network. In addition, a fine-grained attention mechanism is proposed to capture the word-level interactions between plain text and emoji. Finally, we concatenate these features and feed them into a CNN classifier to predict the sentiment labels of the microblogs. To verify the effectiveness of the model and the fine-grained attention network, we conduct comparison experiments and ablation experiments. The comparison experiments show that the model outperforms previous methods in three evaluation indicators (accuracy, precision, and recall) and the model can significantly improve sentiment classification. The ablation experiments show that compared with ALBERT-AET, the proposed model ALBERT-FAET is better in the metrics, indicating that the fine-grained attention network can understand the diversified information of emoticons.
