Twitter Sentimental Analysis & Algorithm Comparison for Uber & Ola Using ‘R’

  • Jyotsna Anthal et al.


Twitter is a social networking site where a large number of users are actively present. Data with
hashtags are popular widely on twitter, hence twitter has large amounts of datasets where user tweet
their reviews. These sentiments are used to understand what opinion do people have about a product
or service through their tweets. The datasets used in this system for extracting tweets are ‘UBER &
OLA’. We understand reviews of people towards different products or services which in turn gives
business insights as to what changes can be done or incorporated. It also helps us in the analysis of
market trends and monetization. In this paper, we propose two models for sentiment analysis based on
Naïve Bayes and Support Vector Machine (SVM). Its purpose is to analyze sentiments more
effectively. This system uses R- statistical programming language to generate outputs. Further, this
paper represents the outputs in Word Cloud. The two classifier algorithms are machine learning
algorithms in which we compare their overall accuracy, precision and recall values.