Abstract

Sentiment Analysis is the Natural Language Processing (NLP) is the active research area due to its vast application like stock market prediction, product re-views etc. The sentiment analysis in the regional languages are required for the film industries to increase their profit. Many existing methods has been applied on the sentiment analysis in the regional languages to increases the performance and still, it lags due in efficiency. In this research, the Bi-directional Recurrent Neural Network (BRNN) is applied to increase the performance of the sentiment analysis in the regional languages. The BRNN method has the advantages of rep-resenting the high and poor resources sentences in the common space and sentiment is analyzed based on the similarity measure. The proposed method is evaluated on the twitter data and compared this with the existing methods such as Random forest and Support Vector Machine (SVM). The proposed BRNN has the overall accuracy of 50.32%, while existing method of SVM has the overall accuracy of 38.73%.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.