Amazon Alexa is a cloud-based voice service developed by Amazon that allows customers to interact with technology. There are currently over 40 million Alexa users around the world, so analyzing user sentiments about Alexa will be a good data science project. So, if you want to learn how to analyze the sentiments of users using Amazon Alexa, this article is for you. In this article, I’ll walk you through the task of Amazon Alexa Reviews Sentiment Analysis Using Python.
Amazon Alexa Reviews Sentiment Analysis using Python
The dataset I’m using for the task of sentiment analysis of Amazon Alexa reviews was collected from Kaggle. It contains data about ratings between 1 and 5, the date of reviews, and customer feedback on their experience with Alexa. So let’s import the necessary Python dataset and libraries that we need for this task:
rating date variation verified_reviews feedback 0 5 31-Jul-18 Charcoal Fabric Love my Echo! 1 1 5 31-Jul-18 Charcoal Fabric Loved it! 1 2 4 31-Jul-18 Walnut Finish Sometimes while playing a game, you can answer... 1 3 5 31-Jul-18 Charcoal Fabric I have had a lot of fun with this thing. My 4 ... 1 4 5 31-Jul-18 Charcoal Fabric Music 1
Let’s start by looking at some of the information in that data to see whether or not we need to change it:
rating feedback count 3150.000000 3150.000000 mean 4.463175 0.918413 std 1.068506 0.273778 min 1.000000 0.000000 25% 4.000000 1.000000 50% 5.000000 1.000000 75% 5.000000 1.000000 max 5.000000 1.000000 rating 0 date 0 variation 0 verified_reviews 0 feedback 0 dtype: int64 Index(['rating', 'date', 'variation', 'verified_reviews', 'feedback'], dtype='object')
The dataset’s rating column contains the ratings given by the users of Amazon Alexa on a scale of 1 to 5, where 5 is the best rating a user can give. So let’s look at the breakdown of ratings given to Amazon Alexa by its users:
From the above figure, we can see that most of the customers have rated Amazon Alexa including all its variants as 5. So it means that most of the customers are happy with Amazon Alexa.
Amazon Alexa Reviews Sentiment Analysis
Now let’s move on to the task of sentiment analysis of Alexa’s reviews. The verified_reviews column of the dataset contains all the reviews given by Amazon Alexa’s customers. So let’s add new columns to this data as positive, negative and neutral by calculating the sentiment scores of the reviews:
rating date variation ... Positive Negative Neutral 0 5 31-Jul-18 Charcoal Fabric ... 0.692 0.000 0.308 1 5 31-Jul-18 Charcoal Fabric ... 0.807 0.000 0.193 2 4 31-Jul-18 Walnut Finish ... 0.114 0.102 0.784 3 5 31-Jul-18 Charcoal Fabric ... 0.383 0.000 0.617 4 5 31-Jul-18 Charcoal Fabric ... 0.000 0.000 1.000
Now let’s sum the sentiment scores for each column to understand what most of the customers of Amazon Alexa think about it:
The final output that we get is therefore neutral. This means that most users feel neutral about Amazon Alexa services. Now let’s see the sum of the sentiment scores for each column:
Positive: 1035.4579999999983 Negative: 96.79999999999995 Neutral: 1936.740999999996
So we can see that Positive and Neutral are above 1000 where Negative is below 100. So this means that most of the customers of Amazon Alexa are satisfied with its services.
So this is how we can analyze the sentiments of Amazon Alexa reviews by using the Python programming language. There are currently over 40 million Alexa users around the world, so analyzing user sentiments about Alexa will be a good data science project. I hope you liked this article on the task of Amazon Alexa Reviews Sentiment Analysis using Python. Feel free to ask your valuable questions in the comments section below.