This article presents our research on understanding the cab services throughout India for Uber & Ola using Deep Learning.

Important Points

1. Our work addresses the mode of transport for different people on a day to day basis and what they expect from the service providers.
2. The two currently popular cab services in India are Uber & Ola with a vast number of users with a different lifestyle.
3. Today Twitter is at a peak of data with millions of people tweeting every day, the current Uber & Ola followers on Twitter are 315.2K & 244.9K respectively.
4. The Deep Learning algorithm used for understanding the sentiments of people is Convolutional Neural Network.
5. We evaluate the model for sentiment classes such as Positive & Negative to find out what accuracy it is generating with respect to the use case.

Introduction

Twitter Sentiment analysis is used to find the sentiments or emotions of people behind the tweet. A review of a person/customer is analyzed via the tweets which helps the companies to further understand what review does a customer has about the product or service provided by the company. From the time Twitter sentiment analysis has started, it has been beneficial a lot for companies to extract, quantify & understand what value their product holds from a customer’s perspective. Although Twitter sentiment analysis can be done for any domains, the domain chosen is Uber & Ola cab riding service companies. The reason for choosing Uber & Ola is because of the vast data which can be collected from the cab users. That can be later used, to extract the tweets to understand if the customers are happy or aren’t with the services & what issues they are facing.

Research has done on the sentiment analysis for 3000 tweets, after extracting them the tweets had to be cleaned for stop words, hyper-links, white spaces. The approach that we thought of using was deep learning to understand more keenly how can it create an impact on Twitter sentiment analysis of Uber & Ola. The algorithms used are Deep Feed Forward Neural Network (DFF) & Convolution Neural Network (CNN) for our data sets. These two algorithms are categories of Deep Neural Network (DNN). After cleaning the tweets, the first technique that is Google Word2Vec is used. Google Word2Vec is an advanced way to train the vocabulary in the text. It trains the vocabulary to the nearest possible meaning of the word. The various parameters are weight multiplication of perceptron, various activation functions, optimizers for optimizing outputs & loss functions. The accuracies are calculated based on the loss function. This function is used to calculate the loss between the training & testing data, thereby making us understand how deep learning algorithms impact the Twitter sentiment analysis for Uber & Ola.

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