使用双向LSTM微调BERT进行细粒度电影评论情感分析
Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis
AI总结：
本文提出用BiLSTM微调BERT模型，在电影评论二元和细粒度情感分类上达到SOTA水平，IMDb准确率97.67%，SST-5准确率59.48%。
摘要：
情感分析（SA）在理解人们观点、促进社交媒体监控、识别产品和品牌以及衡量客户满意度方面发挥着重要作用。因此，SA已成为自然语言处理（NLP）领域内一个活跃的研究方向。文献中描述的许多方法设计了复杂的框架以实现高准确率，仅专注于二元情感分类或细粒度情感分类。在本文中，我们的目标是使用双向LSTM（BiLSTM）微调预训练的BERT模型，以增强针对电影评论的二元和细粒度SA。我们的方法涉及对每条评论进行情感分类，然后计算所有评论的整体情感极性。我们展示了在基准数据集上二元分类和细粒度分类的结果。此外，我们实施并评估了两种准确率提升技术：合成少数类过采样技术（SMOTE）和NLP增强器（NLPAUG），以增强模型在细粒度情感分类中的泛化能力。最后，采用启发式算法从BERT+BiLSTM输出向量计算预测评论的整体极性。我们的方法在两种分类中均与最先进（SOTA）技术表现相当。例如，在二元分类中，我们在著名的IMDb数据集上达到97.67%的准确率，超过领先的SOTA模型NB-weighted-BON+dv-cosine 0.27%。相反，在SST-5上的五类分类中，虽然顶级SOTA模型RoBERTa+large+Self-explaining达到55.5%的准确率，但我们的模型达到59.48%的准确率，超过BERT-large基线3.6%。
英文摘要：
Sentiment Analysis (SA) is instrumental in understanding peoples viewpoints facilitating social media monitoring recognizing products and brands and gauging customer satisfaction. Consequently SA has evolved into an active research domain within Natural Language Processing (NLP). Many approaches outlined in the literature devise intricate frameworks aimed at achieving high accuracy, focusing exclusively on either binary sentiment classification or fine-grained sentiment classification. In this paper our objective is to fine-tune the pre-trained BERT model with Bidirectional LSTM (BiLSTM) to enhance both binary and fine-grained SA specifically for movie reviews. Our approach involves conducting sentiment classification for each review followed by computing the overall sentiment polarity across all reviews. We present our findings on binary classification as well as fine-grained classification utilizing benchmark datasets. Additionally we implement and assess two accuracy improvement techniques Synthetic Minority Oversampling Technique (SMOTE) and NLP Augmenter (NLPAUG) to bolster the models generalization in fine-grained sentiment classification. Finally a heuristic algorithm is employed to calculate the overall polarity of predicted reviews from the BERT+BiLSTM output vector. Our approach performs comparably with state-of-the-art (SOTA) techniques in both classifications. For instance in binary classification we achieve 97.67% accuracy surpassing the leading SOTA model NB-weighted-BON+dv-cosine by 0.27% on the renowned IMDb dataset. Conversely for five-class classification on SST-5 while the top SOTA model RoBERTa+large+Self-explaining attains 55.5% accuracy our model achieves 59.48% accuracy surpassing the BERT-large baseline by 3.6%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
