Sentiment analysis using semi-supervised learning with few labeled data

Sentiment analysis has been widely explored in many text domains, including tweets, movie reviews, shop/restaurant reviews, product reviews, and peer reviews for scholarly papers. However, it is very costly to manually label the training data for sentiment analysis. We focus on the problem and presents an approach for leveraging contextual features from unlabeled movie and restaurant reviews with a neural-network-based learning model, Ladder network. The experimental results by using two benchmark datasets, IMDb and YelpNYC, show that our model outperforms the baseline models including LSTM and SVM. Especially we verified that our model is better performance gaining on limited training datasets with 1% data labeled. Our source codes are available online.
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Our source code can be obtained from https://github.com/jepyh/sentiment_analysis_few_labeled.