Sentiment Analysis of Multilingual Twitter Data using Natural Language Processing

The feelings of WEB users have a great influence on rest of the users, product sellers and market analysis. It is necessary to well structure the unstructured data from various social platforms for proper and meaningful analyses. For the classification of multilingual data, the analysis of feelings has recognized significant attention. This is called textual organization that may be used to classify state of mind or feelings expressed in different ways like: negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. in the field of Automatic Language Processing (NLP). To solve this kind of problem, sentiment analysis and deep learning techniques are two merging techniques. Because of machine learning ability, deep learning models are effectively used for this purpose. Recurrent Neural Networks (RNN) and Naive Bayes algorithm are two popular deep learning architectures to analyze feelings in sentences. These architectures may be used in natural language processing. In this research article, we propose solutions to multilingual sentiment analysis problem by implementing algorithms and in order to contract the result, we compare precision factor to find the best solution for multilingual sentimental analysis.