Amazon is an American multinational corporation that focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence products. But it is mainly known for its e-commerce platform which is one of the biggest online shopping platforms today. There are so many customers buying products from Amazon that today Amazon earns an average of $ 638.1 million per day. So having such a large customer base, it will turn out to be an amazing data science project if we can analyze the sentiments of Amazon product reviews. So, in this article, I will walk you through the task of Amazon Product Reviews Sentiment Analysis with Python.
Amazon Product Reviews Sentiment Analysis with Python
The dataset I’m using for the task of Amazon product reviews sentiment analysis was downloaded from Kaggle. This dataset contains the product reviews of over 568,000 customers who have purchased products from Amazon. So let’s start this task by importing the necessary Python libraries and the dataset:
Id ProductId UserId ProfileName \
0 1 B001E4KFG0 A3SGXH7AUHU8GW delmartian
1 2 B00813GRG4 A1D87F6ZCVE5NK dll pa
2 3 B000LQOCH0 ABXLMWJIXXAIN Natalia Corres "Natalia Corres"
3 4 B000UA0QIQ A395BORC6FGVXV Karl
4 5 B006K2ZZ7K A1UQRSCLF8GW1T Michael D. Bigham "M. Wassir"
HelpfulnessNumerator HelpfulnessDenominator Score Time \
0 1 1 5 1303862400
1 0 0 1 1346976000
2 1 1 4 1219017600
3 3 3 2 1307923200
4 0 0 5 1350777600
Summary Text
0 Good Quality Dog Food I have bought several of the Vitality canned d...
1 Not as Advertised Product arrived labeled as Jumbo Salted Peanut...
2 "Delight" says it all This is a confection that has been around a fe...
3 Cough Medicine If you are looking for the secret ingredient i...
4 Great taffy Great taffy at a great price. There was a wid...
Before moving forward, let’s take a look at some of the information needed from this dataset:
print(data.describe())
Id HelpfulnessNumerator HelpfulnessDenominator \
count 568454.000000 568454.000000 568454.00000
mean 284227.500000 1.743817 2.22881
std 164098.679298 7.636513 8.28974
min 1.000000 0.000000 0.00000
25% 142114.250000 0.000000 0.00000
50% 284227.500000 0.000000 1.00000
75% 426340.750000 2.000000 2.00000
max 568454.000000 866.000000 923.00000
Score Time
count 568454.000000 5.684540e+05
mean 4.183199 1.296257e+09
std 1.310436 4.804331e+07
min 1.000000 9.393408e+08
25% 4.000000 1.271290e+09
50% 5.000000 1.311120e+09
75% 5.000000 1.332720e+09
max 5.000000 1.351210e+09
As this dataset is very large, it contains some missing values, so let’s remove all the rows containing the missing values:
data = data.dropna()
Sentiment Analysis of Amazon Product Reviews
The Score column of this dataset contains the ratings that customers have given to the product based on their experience with the product. So let’s take a look at the rating breakdown to see how most customers rate the products they buy from Amazon:
According to the figure above, more than half of people rated products they bought from Amazon with 5 stars, which is good. Now, I’m going to add three more columns to this dataset as Positive, Negative, and Neutral by calculating the sentiment scores of the customer reviews mentioned in the Text column of the dataset:
Id ProductId UserId ... Positive Negative Neutral 0 1 B001E4KFG0 A3SGXH7AUHU8GW ... 0.305 0.000 0.695 1 2 B00813GRG4 A1D87F6ZCVE5NK ... 0.000 0.138 0.862 2 3 B000LQOCH0 ABXLMWJIXXAIN ... 0.155 0.091 0.754 3 4 B000UA0QIQ A395BORC6FGVXV ... 0.000 0.000 1.000 4 5 B006K2ZZ7K A1UQRSCLF8GW1T ... 0.448 0.000 0.552 [5 rows x 13 columns]
Now let’s see how most people rated the products they bought from Amazon:
Neutral 🙂
So, most people are neutral when submitting their experiences with the products they have purchased from Amazon. Now let’s see the total of all sentiment scores:
Positive: 109328.1269999992 Negative: 24033.022999999564 Neutral: 435043.95799998916
So we can say that most of the reviews of the products available on Amazon are positive, as the total sentiment scores of Positive and Neural are much higher than Negative scores.
Summary
So this is how we can analyze the sentiments of the product reviews at amazon. There are so many customers buying products from Amazon that today Amazon earns an average of $ 638.1 million per day. So having such a large customer base, it will turn out to be an amazing data science project if we can analyze the sentiments of Amazon product reviews. I hope you liked this article on Amazon Product Reviews Sentiment Analysis with Python. Feel free to ask your valuable questions in the comments section below.
