用混合深度学习模型提升乌尔都语文档级情感分析准确率
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques
- 设计了融合BiLSTM与多滤波卷积的混合模型
- 在多个数据集上达到最高94%的分类准确率
- 适合资源匮乏语言的情感分析研究者参考
文档级乌尔都语情感分析是自然语言处理中一项挑战性任务，因其涉及资源匮乏语言的大规模文本。在长文档中，存在大量表达不同观点的词汇。深度学习模型通过复杂的神经网络结构，能够学习数据的多样化特征以实现情感分类。除音频、图像和视频分类外，深度学习算法现广泛应用于文本分类。为探索适用于乌尔都语情感分析的强大深度学习技术，本文采用五种架构：双向长短期记忆网络（BiLSTM）、卷积神经网络（CNN）、CNN-BiLSTM、BERT，并提出一种新混合模型——将BiLSTM与单层多滤波卷积神经网络（BiLSTM-SLMFCNN）结合。所提模型及基线方法在乌尔都语客服数据集和IMDB乌尔都语电影评论数据集上进行测试，使用预训练的乌尔都语词嵌入。实验结果表明，该模型优于所有基线方法，在小型、中型、大型IMDB乌尔都语电影评论数据集以及乌尔都语客服数据集上分别取得83%、79%、83%和94%的准确率。
原文摘要 · Abstract (English)
Document level Urdu Sentiment Analysis (SA) is a challenging Natural Language Processing (NLP) task as it deals with large documents in a resource-poor language. In large documents, there are ample amounts of words that exhibit different viewpoints. Deep learning (DL) models comprise of complex neural network architectures that have the ability to learn diverse features of the data to classify various sentiments. Besides audio, image and video classification; DL algorithms are now extensively used in text-based classification problems. To explore the powerful DL techniques for Urdu SA, we have applied five different DL architectures namely, Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), Bidirectional Encoder Representation from Transformer (BERT). In this paper, we have proposed a DL hybrid model that integrates BiLSTM with Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN). The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pretrained Urdu word embeddings that are suitable for (SA) at the document level. Results of these techniques are evaluated and our proposed model outperforms all other DL techniques for Urdu SA. BiLSTM-SLMFCNN outperformed the baseline DL models and achieved 83{\%}, 79{\%}, 83{\%} and 94{\%} accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.
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