Sentiment Classification of Food Reviews
This work addresses sentiment analysis for food reviews, but it is incremental as it applies standard methods to a specific domain.
The paper tackled sentiment classification of food reviews on a 1-5 scale by tuning recurrent neural networks, including RNN and GRU, and addressing data skewness, with results evaluated using accuracy metrics.
Sentiment analysis of reviews is a popular task in natural language processing. In this work, the goal is to predict the score of food reviews on a scale of 1 to 5 with two recurrent neural networks that are carefully tuned. As for baseline, we train a simple RNN for classification. Then we extend the baseline to GRU. In addition, we present two different methods to deal with highly skewed data, which is a common problem for reviews. Models are evaluated using accuracies.