Enhancing Sentiment Analysis with a CNN-Stacked LSTM Hybrid Model - Shao Shuaijie (ITM Web of Conferences)
This paper focuses on developing a new hybrid model to solve sentiment analysis problems in Natural language processing. Sentiment analysis is a key branch of Natural language processing (NLP) and new models with better performance can boost the development of machine learning. The new model mentioned in this research is a hybrid model containing convolutional neural network (CNN), stacked multi-layer long short-term memory (LSTM) and max pooling layers. This model uses CNN for its advantage of capturing local features in the sequence after the embedding process, and LSTM for its advantage of capturing long-term dependencies in such sequential data after CNN layer. The global max pooling layer can better organize the entire sequence. This model has been tested to show that it has a better performance than previously mentioned models when solving the sentiment analysis task based on IMDB dataset provided by TensorFlow. Introducing this new model in sentiment analysis may open new avenues for research. The performance of the model can be further improved, offering valuable insights for future hybrid model development in machine learning tasks.