The document analyzes a dataset of nearly 3000 Amazon customer reviews for Alexa products, detailing the review variables like star ratings, review dates, product variants, and feedback. It focuses on sentiment analysis and regression analysis to uncover insights about customer opinions and product performance. Conclusions drawn from the analysis indicate a high frequency of 5-star ratings, showcasing customer satisfaction with the products.

1. R Programming
Topic: Analyzing a dataset

2. Dataset: Amazon Alexa Reviews

3. Introduction
 This dataset consists of a nearly 3000 Amazon customer reviews (input text),
and 5 variables which are star ratings, date of review, variant, verified reviews
and feedback of various Amazon Alexa products like Alexa Echo, Echo dots,
Alexa Firesticks etc.

4. Explanation of variables:
1. Star ratings(num): The star ratings are the ratings given by customers to the various
products of amazon after testing and using them and generally range from 1 to 5.
2. Date of review(num): The date on which the customers are reviewing the various products
of amazon.
3. Variation(char): The different variants of the products of amazon. For E.g.: Black variant of
echo show & white variant of echo show are two different variants of echo show.
4. Verified reviews(char): These reviews are given by customers or official personnel who
have genuinely tested the various amazon products and are marked as verified by Amazon.
5. Feedback(num): This variable is having two face value i.e. 1 and 0.
• 1 indicates a positive feedback from the customer.
• 0 indicates a negative feedback from the customer.

5. Amazon Alexa
 Amazon Alexa known simply as Alexa is a virtual assistant developed by Amazon,
first used in the Amazon Echo and the Amazon Echo Dot smart speakers developed
by Amazon Lab126.
 It is capable of voice interaction, music playback, making to-do lists, setting alarms,
streaming podcasts, playing audiobooks, and providing weather, traffic, sports, and
other real-time information, such as news.
 Alexa can also control several smart devices using itself as a home
automation system.

6. Objectives
 Discover insights into consumer reviews and assist with machine learning models.
 Train your machine models for sentiment analysis.
 Analyze customer reviews how many positive reviews .
 How many negative reviews.
 Bringing out relation between variables.
 Summarizing data and finding patterns.
 Analyzing patterns through plot of graph.

7. Sentiment Analysis
 Sentiment analysis is the measurement of positive and negative language. Sentiment
analysis is also known as opinion mining.
 It is a way to evaluate written or spoken language to determine if the expression is
favorable, unfavorable, or neutral, and to what degree.
 Sentiment analysis uses data mining processes and techniques to extract and capture
data for analysis in order to discern the subjective opinion of a document or collection
of documents, like blog posts, reviews, news articles and social media feeds like tweets
and status updates.

8. Sentiment
analysis for
facebook
Sentiment
analysis for
twitter
Intent analysis
of facebook
comments

9. install.packages(“kSentiment”)
library(kSentiment)
 Using these commands you can install
the Sentiment package and can
perform sentiment analysis commands.
Applications of
sentiment analysis

10. Features of Amazon Alexa

11. Products of Amazon
ECHO DOT
ECHO PLUS AMAZON ECHO
ECHO SPOT

12. FIRE TV STICK ECHO SHOW
Based on their experience, the customers give reviews, feedback and
star ratings to the various products they have used and tested.

13. Structure & Summary function

14. Vector function

15. which.min & which.max function

16. Subset Function

18. Plot, histogram & boxplot function

19. Most widely used
variations are black
dot and charcoal
fabric.
install.pakage(“tidyverse”)
library(tidyverse)

20. hist(amazon_alexa$i..rating,
xlab=“Alexa Rating”,
ylab=“Number of Ratings”,
main=“Bar Chart of Alexa
Ratings”)

21. install.packages(“tidytext”)
library(tidytext)
unnest_tokens() function

23. Table function

24. Tapply () function
 Syntax: Tapply(x, INDEX, FUN = NULL, …, default = NA, simplify = TRUE)
1. X = a vector,
2. INDEX = list of one or more factor,
3. FUN = function or operation that needs to be applied.
Lets understand with data :-
Now we have to summarize data by date with rating to see interesting patterns

26. Regression Analysis
• Regression Analysis is a very widely used statistical tool to establish a relationship model
between two variables. One of these variable is called predictor variable whose value is gathered
through experiments. The other variable is called response variable whose value is derived from
the predictor variable.
• Syntax: Lm(formula,data)
• Formula is a symbol presenting the relation between x and y.
• Data is the vector on which the formula will be applied.
• Adjusted r square:-the adjusted r-squared compares the explanatory power of regression models
that contain different numbers of predictors.
• Interpret the p-values:-a low p-value (< 0.05) indicates that you can reject the null hypothesis. In
other words, a predictor that has a low p-value is likely to be a meaningful addition to your model
because changes in the predictor's value are related to changes in the response variable.

27. Linear regression

29. Conclusion
 By analyzing this dataset named “Amazon Alexa” we can give the potential
customers a clear understanding of the product by providing them the reviews of
those customers who already have Alexa.
 This analysis shows that the products star ratings have maximum frequency at 5 star
and minimum frequency at 2 star, this takes us to the conclusion that the product is
liked by the customers.
 From the histogram we had plotted for the variable star ratings, a conclusion can be
drawn that almost 2500 customers out of the total 3150 customers have given a 5-star
rating to the various products of amazon, thus making it the most frequent star rating
given by the customers to the products.

30.  Relationships between the different variables were analyzed.
 The data was summarized and interesting patterns were obtained.
 These patterns were then further analyzed by using various plot functions such as
histogram, boxplot, etc.

31. Thank You!!