Amazon Echo Reviews Sentiment Analysis Using NLP

Our project is all about understanding what people think about Amazon Echo by

looking at their reviews. We're using Python as a programming language and for

the ability of a computer program to understand human language as it's spoken

and written we are going to use Natural Language Processing (NLP) to do this, and

to Train and test we’ll use Machine learning model called the Naive Bayes

Classifier, we want to guess if customers are happy or not based on what they say

in their reviews. This project is super important because it helps companies make

their products and services better by listening to what customers have to say. We

chose Python because it has really good tools for understanding language, and

the Naive Bayes Classifier is great at figuring out if reviews are positive or negative

Our main goal is to help companies understand how customers feel about

Amazon Echo by analyzing their reviews. We use a smart system to read and

understand the words in the reviews. First, We’ll do things like getting rid of

common words that don't tell us much and breaking down the text into smaller

parts to get a better idea. Then, we use the Naive Bayes Classifier to guess if

customers are happy or not. By doing this, companies can save time and quickly

find out if customers like the product. This helps them make better decisions and

improve their products to make customers happier.

• Data preprocessing: Removing common words, and breaking down text

into smaller parts.

• Sentiment analysis: using the Naive Bayes Classifier.

Expected Input and Output:

• Input: Amazon Echo reviews as a dataset.

• Output: Sentiment analysis to analyze and guess whether customers are

happy or not.

• Libraries Expected to Be Used:

1. Pandas: Data manipulation for data CSV files .

2. Numpy: Efficient numerical computing with support for large arrays

3. Seaborn: Creating informative statistical visualizations

4. Matplotlib.pyplot: Generating various types of plots for data visualization

5. jupyterthemes.jtplot: Customizing the appearance of Jupyter Notebooks for

better readability.

6. Wordcloud: Generating word clouds to visually represent word frequencies

in text data.

7. NLTK (Natural Language Toolkit): Providing tools and resources for natural

language processing tasks.

8. Sklearn (scikit-learn): Implementing machine learning algorithms and tools

for data mining and analysis.

NLTK (Natural Language Toolkit): Stopwords corpus will be used for removing

common words from text data.

In conclusion, our project is all about making things easier for companies by

understanding what customers think about Amazon Echo. With the help of

Python, NLP, and the Naive Bayes Classifier, we're helping businesses listen to

their customers and make better products. It's all about using smart technology

to make everyone happier.