Sentiment Analysis Using Twitter
NATURAL LANGUAGE PROCESSING
VS
Sentiment Comparison between the 2 Giant companies
Objective
The objective of this sentiment analysis is to understand customer’s review/opinion for both the
organizations. To understand which company is more liked/loved by the customers.
First we have extracted tweeter data in R platform(around 2500 tweets).
Next we cleaned the data and gave it a structural form for our analysis.
Then, we made 3 world clouds, to understand the frequencies of each kind of words: 1) for the whole data, 2) For Positive response in favor of the organization. 3) Negative response of the organization.
Next we counted the occurrence of word and plotted a histogram to understand the distribution of words following its occurrence in twitter.
Then, we used SVM (machine learning algorithm to find out the accuracy)
Package used tweetR, stringr, ROAuth, plyr, ggplot2,tm, RcolorBrewer, httr, wordcloud, sentimentr, Rcurl, snowball, e1071
Methodology/Process
WordCloud of Flipkart and Amazon
Positive and Negative WordCloud of Flipkart andAmazon
Positive and Negative WordCloud of Flipkart andAmazon
Visualization using HistogramVisualization using Histogram
Interpretation of Analysis
The mean Score of total word count of both the organization is positive, which indicates that there are positive reactions for both the organization.
We have classified the response using SVM ( 0- positive, 1- negative) with 87.40% and 84.50%.
Interpretation of Analysis
Conclusion
Both the organization has positive customer response with positive mean value.
But, Amazon has higher positive response rate when compared to Flipkart, which indicates that currently Amazon is ruling the market
with higher sales and revenue/profit (as customers have a positive reviews for it).
Conclusion