A Religious Sentiment Detector Based on Machine Learning to Provide Meaningful Analysis of Religious Texts

This paper has the sole purpose of showing the fact that religious sentiment detection holds an important place in the industry and our efforts have been totally concerned with easing the problems of the industry which have been existing to date. Our research has been focused on the shortcomings of various previous methods which have been suggested by previous researchers to classify religious sentiments. There has never been any single application that can classify the sentiments present in a given block of religious text by analyzing only the religious text. We have designed the model in such a way that the users will not have to specify the religious texts and filter them out. The application will reject all the nonreligious texts and provide the desired outcome after analyzing the given religious text which is provided by the user. The model works on the basis of Natural Language Processing and it is able to handle a large amount of data. It is trained using the data sets of the 12 main religions of the world and it is able to perform predictive analysis of the input text since the model is trained using RNN and LSTM algorithms. We have also used the KNN algorithm in the testing phases of the model. A brief analysis of the time complexity along with the comparison of performance evaluation among the different methods have also been discussed in this paper. In the results that we received, it can be clearly seen that we have achieved a minimum loss of 0.083, and the highest accuracy value of the model is found to be 99.8%, This study evaluates the different approaches that can be used to perform sentiment analysis on religious texts and provides a landmark for future researchers to continue improvements in this field. Our research paves the way for future researchers to work more on the untouched portions of sentiment analysis and its applications in real life. In this way, these extensive technologies can be put to better use. We believe that our work and our results might be able to help the people in common to get rid of the harmful and malicious effects of certain religious texts and they would be able to recognize the religious texts which carry good value or provide comfort to them.