Sentiment analysis Amazon aims to know how folks are feeling concerning an object or topic immediately by analyzing historical data on that object/topic. Commerce.ai is the best platform that helps sellers to understand customers' expectations about a product.

2. Importance Of Sentiment Analysis For
Amazon Product Reviews
Commerce.AI
Sentiment Analysis Amazon is one of the key areas where NLP has
been widely used. The most prevalent text categorization tool is
sentiment analysis, which analyzes an incoming message and
determines if the underlying sentiment is positive, negative, or
neutral. Organizations must understand client behavior and
demand for their products and services.
In general, a product review on Amazon can be divided into three
categories: positive, negative, and neutral. Companies can use
product reviews to interpret customer feedback and determine
how satisfied customers are with their products and services.
Because customers can communicate their opinions, and feelings
more openly than ever before, understanding people’s emotions is
essential for organizations. It’s difficult for a human to go over each
line and determine the emotion expressed by the user experience.
It allows businesses to listen carefully to their customers and
customize products and services to match their expectations.
Sentiment analysis can be classified into three types.
1. Predictive sentiment analysis: a sentiment is a descriptive
expression of an idea or feeling. Sentiment analysis is the process

3. of predicting or extracting these ideas or feelings with this in mind.
We’re looking for positive, negative, or neutral sentiment in a piece
of writing. The actual definition of positive/negative sentiment
changes depending on the problem at hand.
• This type of sentiment analysis is used to better understand
future behavior rather than make decisions.
• Product developers can use predictive sentiment analysis to
determine the general public’s interest in a product and make
decisions based on that information (e.g., they can decide on
the promotion strategy for the product).
2. Diagnostic sentiment analysis: This type of sentiment analysis
aims to know how folks are feeling concerning an object or topic
immediately by analyzing historical data on that object/topic.
• This type of sentiment analysis is used to analyze problems
and determine trends in data that affect decision-making.
• While predictive sentiment analysis predicts how folks will
feel in the future, diagnostic sentiment analysis investigates
issues and identifies trends in data that affect decision-
making.
• 3. Sentiment classification: This type of classification can be
helpful when determining whether a piece of text is positive
or negative based on its content,
Popular datasets for sentiment analysis

4. Several datasets can be used for sentiment analysis, though some
are higher-quality and more insightful than others.
1. Amazon Review Data: Another popular source is Amazon
Review Data which contains data from 1996 to 2018 that analysts
can use to analyze product reviews and learn how individuals feel
about the products they’re buying. Ratings, text, helpful votes,
product descriptions, category information, price, brand, and
image attributes are all included in the dataset reviews.
2. Sentiment 140 Through user tweets on the social media network
Twitter, the Sentiment140 dataset for sentiment analysis is used to
analyze user responses to different products, brands, or topics.
This dataset enables sentiment analysis using a set of words that
were recognized as also being indicative of positive or negative
sentiment. Many researchers in the field of sentiment analysis have
analyzed the dataset, which consists almost entirely of English
tweets.
3. The Stanford Sentiment Treebank: SST is well-regarded as an
important dataset because of its ability to check an NLP model’s
abilities in sentiment analysis. This resource includes fine-grained
sentiment labels for over 200,000 phrases and nearly 12,000
sentences. The Stanford Sentiment Treebank helped to push
progressive performance in sentiment label classification.
It’s essential to conduct an Amazon product review analysis to
ensure that there is a market for the product and its specific

5. features, as well as to determine merchandising methods, for a
new product to succeed.