Leveraging Sparse and Dense Feature Combinations for Sentiment Classification
This addresses sentiment analysis for NLP applications, but appears incremental as it builds on existing neural network approaches.
The authors tackled sentiment classification by proposing a simple, robust model that outperforms many deep learning models and achieves comparable results to complex architectures on sentiment analysis datasets.
Neural networks are one of the most popular approaches for many natural language processing tasks such as sentiment analysis. They often outperform traditional machine learning models and achieve the state-of-art results on most tasks. However, many existing deep learning models are complex, difficult to train and provide a limited improvement over simpler methods. We propose a simple, robust and powerful model for sentiment classification. This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets. We publish the code online.