Effectively analyzing the comments to uncover latent intentions holds immense value in making strategic decisions across various domains. However, several challenges hinder the process of sentiment analysis including the lexical diversity exhibited in comments, the presence of long dependencies within the text, encountering unknown symbols and words, and dealing with imbalanced datasets. Moreover, existing sentiment analysis tasks mostly leveraged sequential models to encode the long dependent texts and it requires longer execution time as it processes the text sequentially. In contrast, the Transformer requires less execution time due to its parallel processing nature. In this work, we introduce a novel hybrid deep learning model, RoBERTa-BiLSTM, which combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks. RoBERTa is utilized to generate meaningful word embedding vectors, while BiLSTM effectively captures the contextual semantics of long-dependent texts. The RoBERTa-BiLSTM hybrid model leverages the strengths of both sequential and Transformer models to enhance performance in sentiment analysis. We conducted experiments using datasets from IMDb, Twitter US Airline, and Sentiment140 to evaluate the proposed model against existing state-of-the-art methods. Our experimental findings demonstrate that the RoBERTa-BiLSTM model surpasses baseline models (e.g., BERT, RoBERTa-base, RoBERTa-GRU, and RoBERTa-LSTM), achieving accuracies of 80.74%, 92.36%, and 82.25% on the Twitter US Airline, IMDb, and Sentiment140 datasets, respectively. Additionally, the model achieves F1-scores of 80.73%, 92.35%, and 82.25% on the same datasets, respectively.

翻译：有效分析评论以揭示潜在意图，对于各领域的战略决策具有重要价值。然而，情感分析过程面临诸多挑战，包括评论中表现出的词汇多样性、文本内存在的长距离依赖关系、未知符号和词汇的出现，以及不平衡数据集的处理。此外，现有的情感分析任务大多利用序列模型对长依赖文本进行编码，由于其顺序处理文本的特性，需要较长的执行时间。相比之下，Transformer因其并行处理特性而需要更少的执行时间。在本工作中，我们提出了一种新颖的混合深度学习模型RoBERTa-BiLSTM，该模型将鲁棒优化的BERT预训练方法（RoBERTa）与双向长短期记忆（BiLSTM）网络相结合。RoBERTa用于生成有意义的词嵌入向量，而BiLSTM则有效捕捉长依赖文本的上下文语义。RoBERTa-BiLSTM混合模型结合了序列模型和Transformer模型的优势，以提升情感分析性能。我们使用IMDb、Twitter US Airline和Sentiment140数据集进行了实验，以评估所提模型相对于现有先进方法的性能。实验结果表明，RoBERTa-BiLSTM模型在Twitter US Airline、IMDb和Sentiment140数据集上分别达到了80.74%、92.36%和82.25%的准确率，超越了基线模型（如BERT、RoBERTa-base、RoBERTa-GRU和RoBERTa-LSTM）。此外，该模型在相同数据集上分别实现了80.73%、92.35%和82.25%的F1分数。