Oslcfit (organic simultaneous LSTM and CNN Fit): A novel deep learning based solution for sentiment polarity classification of reviews

Abstract

Review sentiment influences purchase decisions and indicates user satisfaction. Inferring the sentiment from reviews is an essential task in Natural Language Processing and has managerial implications for improving customer satisfaction and item quality. Traditional approaches to polarity classification use bag-of-words techniques and lexicons combined with machine learning. These approaches suffer from an inability to capture semantics and context. We propose a Deep Learning solution called OSLCFit (Organic Simultaneous LSTM and CNN Fit). In our architecture, we include all the components of a CNN until but not including the final fully connected layer and do the same in case of a bi-directional LSTM. The final fully connected layer in our architecture consists of fixed length features from the CNN, and features for both variable length and temporal dependencies from the bi-directional LSTM. The solution fine-tunes Language Model embeddings for the specific task of polarity classification using transfer learning, enabling the capture of semantics and context. The key contribution of this paper is the combination of features from both a CNN and a bi-directional LSTM into a single architecture with a single optimizer. This combination forms an organic combination and uses embeddings fine-tuned to the reviews for the specific purpose of sentiment polarity classification. The solution is benchmarked on six different datasets such as SMS Spam, YouTube Spam, Large Movie Review Corpus, Stanford Sentiment Treebank, Amazon Cellphone & Accessories and Yelp, where it beats existing benchmarks and scales to large datasets. The source code is available for the purposes of reproducible research on GitHub.11https://github.com/efpm04013/finalexp34