A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment Analysis

Lanxue Dang, Chunyu Wang, Hongyu Han, Yan-E. Hou

Abstract: Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset.