BowTie - A deep learning feedforward neural network for sentiment analysis
This work addresses accuracy and transferability issues in sentiment analysis, though it appears incremental as it builds on existing neural network methods.
The authors tackled the problem of sentiment analysis by developing a computationally-efficient feedforward neural network that maintains low losses, achieving high accuracy on benchmark datasets.
How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model. I present a computationally-efficient and accurate feedforward neural network for sentiment prediction capable of maintaining low losses. When coupled with an effective semantics model of the text, it provides highly accurate models with low losses. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new approach.