Sentiment Analysis Using Rnn and Google Translator

Abstract

Sentimental analysis is the term which is used to refer the usage of NLP (Natural language processing), Analysis of text, linguistics related to computers or machines and the metrics of biology termed as biometrics. Sentimental analysis is carried out for the sole purpose of the reviews or surveys of a product or a person which are in conclusion to the sentiments of people involved for that particular product or customer. Opinion Mining which comes under the Sentimental analysis or sometimes termed as same also uses the techniques of machine learning which involves those algorithms which help us to find the review of the product or service which is in return helpful for the customers or buyers and also for the sellers which can help them to improve their product. This research paper, s focal points are the techniques which involves NLP(Natural language processing) in which there is a usage of Stanford Library for increasing the competence power of the machine to classify more data which is fetched from Twitter using the twitter Application program interface, since twitter now a days is that platform where you can find out the tweets about a particular person or the product. So, it becomes a very successful platform to fetch data. Google Translator is used for taking the account of that data also which is not in English. Sometimes fetching data involves those sentences which are not in English so to increase the accuracy we use Google Translator Application program Interface in our project. For the classification of sentiments whether being positive, negative or neutral we use the best algorithm of Neural networks rather than using algorithms like Maximum entropy or naive bayes. By using all these techniques we try to find the maximum accuracy than others.