A Sentiment Analysis System for the Hindi Language by Integrating Gated Recurrent Unit with Genetic Algorithm

Abstract

The growing availability and popularity of opinion rich resources such as blogs, shopping websites, review portals, and social media platforms have attracted several researchers to perform the sentiment analysis task. Unlike English, Chinese, Spanish, etc. the availability of Indian languages such as Hindi, Telugu, Tamil, etc., over the web have also been increased at a rapid rate. This research work understands the growing popularity of Hindi language in the web domain and considered it for the task of sentiment analysis. The research work analyses the hidden sentiments from the movie reviews collected from the review section of Hindi language e-newspapers. The reviews are multilingual, which makes sentiment analysis a challenging task. To overcome the challenges, this research work proposes a deep learning based approach where a Gated Recurrent Unit network is combined with the Hindi word embedding model. The strategy enables the network to efficiently capture the semantic and syntactic relation between Hindi words and accurately classify them into the sentiment classes. Gated Recurrent Unit network's performance is profoundly dependent upon the selection of its hyper-parameters; therefore, this research work also utilizes a Genetic Algorithm to automatically build a gated recurrent network architecture enabling it to select the best optimal hyper-parameters. It has been observed that the proposed Genetic Algorithm-Gated Recurrent Unit (GA-GRU) model is effective and achieves breakthrough performance results on the Hindi movie review dataset as compared to other traditional resource-based and machine learning approaches