Sentiment Analysis of Uber & Ola using Deep Learning

Sentiments are the basic emotions of the human and understanding it for various problems is just like a feature extraction, where different sentiments are taken into consideration for finding problems and understanding the use case. For understanding the sentiments there are various methods, in this, the sentiment analysis is done based on twitter which uses Uber & Ola, which are part of cab services. Understanding the needs and complaints of cab service can help in boosting and solving various problems related to users that are tweeted. Deep learning plays an important role in using tweets and extracting information. Google Word2Vec is also used to generate the vocabularies between the words and make them understand the similarity, which in turn will give better outputs for the tweets. The tweets are categorized in Positive & Negative. The two deep learning algorithms used for sentimental analysis are part of a multi-layer perceptron, which are Deep Feed Forward Neural Network and Convolution Neural Network. The algorithms are trained on training data and accuracy is generated to find if it was trained properly or not. The testing data is used to check if it predicts the correct output which will tell if the algorithm is suited for the datasets or not. The programming language used for sentimental analysis is none other than python.