Sentiment Analysis

Below are my Primary Objectives:

• Understanding the Data
• Analyze the Data
• Learning R, RMD, ShinyApps, and RPres
• Understanding the Different Packages of R
• Data Manipulation, Data Cleaning and Data Visualization
• Applications of R, RMD, ShinyApps and Rpres

Nowadays Social Media is having huge user base and the data posted by their users can be used to find out the expression/feelings/emotions of the people regarding particular thing. Data Source: Twitter, Facebook, Instagram, LinkedIn

• Create off-line a file (this should be simulated as Twitter tweets).
• This file contains one tweet per line.
• Create Shiny App to upload & analyze Polarity & Emotions for tweets.
• Polarity (Positive, Negative, Neutral)
• Emotions( Anger, Joy, Disgust, Fear, Surprise, Fear, Sad)

Different Analytics and approaches used to get the insights of Data:

• Cleaning of the Data
• Line Count, Total Word count, Average no per Line
• Significant Words woth their Frequency
• Sentiment Analysis of each line (Positive, Negative or Neutral)
• Polarity and Emotion Analysis of the Data (Anger, Disgust, Fear, Joy, Sadness, Surprise, Unknown)
• Polarity and Emotion Frequency Graphs
• Word Plot of Significant Words
• Word Cloud of Significant Words
  Business Analytics with R

By the completion of this project my Objective has been met to understanding the data analyze the data, Learning R, ShinyApp, RPres. It helps me to know more about the data Cleaning and how it can be done through R.

It helped me to unserstand the Sentiment Analysis of the Social Media data using R and ShinyApp.

It was an exciting journey, At starting it was started with basics of programming to full technical knowledge of R and others tools.

There were lot of ups and downs due to complexity of the problems but it helped us a lot to get the best and understanding the same.