Sentiment Analysis System using Hybrid Word Embeddings with Convolutional Recurrent Neural Network

Abstract

There have been wide ranges of innovations in sentiment analysis in recent past, with most effective ones involving use of various word embeddings methods for analysis of sentiments. GloVe and Word2Vec are acclaimed to be two most frequently used. A common problem with simple pre-trained embedding methods is that these ignore information related to sentiments of input texts and further depend on large text corpus for training purpose and generation of relevant vectors which is hindrance to researches involving smaller sized corpuses. The aim of proposed study is to propose a novel methodology for sentiment analysis that uses hybrid embeddings with a target to enhance features of available pre-trained embedding. Proposed hybrid embeddings use Part of Speech (POS) tagging and word2position vector over fastText with varied assortments of attached vectors to the pre-trained embedding vectors. The resultant form of hybrid embeddings is fed to our ensemble network-Convolutional Recurrent Neural Network (CRNN). The methodology has been tested for accuracy via different Ensemble models of deep learning and standard sentiment dataset with accuracy value of 90.21 using Movie Review (MVR) Dataset V2. Results show that proposed methodology is effective for sentiment analysis and is capable of incorporating even more linguistic knowledge-based techniques to further improve results of sentiment analysis.