This document summarizes a project on detailed classification of customer reviews using sentiment analysis. It discusses using the VADER and RoBERTa models to analyze sentiment in customer reviews. The methodology section explains how each model works, with VADER using a lexicon-based approach and RoBERTa being a transformer model. Testing results on positive and negative reviews are presented to compare the polarity scores from each model, finding that RoBERTa scores are more confident due to its deep learning approach. The conclusion discusses how sentiment analysis can be helpful for businesses but also has limitations, and cannot alone determine a company's success.

1. Title of the Project:-
Detailed Classification of Customer Reviews
Using SentimentAnalysis
G.H Raisoni College of Engineering, Nagpur
Department of Information Technology and
Engineering
Project Phase –I
:

2. ABSTRACT
 With the rapid of Growth of E-Commerce websites,
there is no doubt that people are drawn to online
shopping more nowadays. As people are drawn to it, so
are the sellers. But sellers can differentiate in quality as
well as quantity.
 To make it easier for consumers to decide which product
suits for their demands and needs, customer review on
E-Commerce Websites is a proficient way for consumers
to get what they are looking for

4. INTRODUCTION
 Sentiment analysis (or opinion mining) is a natural language
processing (NLP) technique used to determine whether data is
positive, negative, or neutral.
 Sentiment analysis is often performed on text data to help
businesses monitor brand and product sentiment in customer
feedback and understand customer needs.

5. • The goal is to automatically recognize and classify opinions expressed in text to determine overall sentiment.
Sentiment analysis is the process of analyzing online writing to determine whether it is positive, negative, or neutral. Simply put,
sentiment analysis helps find the author's attitude towards a topic.
INTRODUCTION

6. Objective
We will be using Concepts likeVader and RoBERTa Model and comparing the results
between these two models.
• Vader Model:
Vader is model that is based on lexicon and rule based matching , sentiment analysis tool
that is specifically aware to sentiments expressed in social platform.
The VADER sentimental analysis uses a dictionary that converts lexical data into sentiment
scores, which measure the intensity of an emotion. By adding the intensity of each word in a
text, one can determine the sentiment score of that text.
• RoBERTa Model:
RoBERTa stands for Robustly Optimized BERT Pre-training Approach.
The goal of this model is to optimize the training of BERT architecture in order to take lesser
time during pre-training.

7. Methodology

8. Methodology
VADER Model:
This model for text sentiment is sensitive to both the polarity (positive/negative)
and intensity (strong) of emotion.TheVADER sentimental analysis uses a
dictionary that converts lexical data into sentiment scores, which measure the
intensity of an emotion. By adding the intensity of each word in a text, one can
determine the sentiment score of that text.

9. POSTaggingTable:
• Part-of-speech (POS) is the practice of
categorizing words in a text (corpus) in
accordance with a certain part of
speech, depending on the word's
definition and context, is known as
tagging in natural language processing

10. Working of
RoBERTa
Model

11. RoBERTa Model
 RoBERTa Model is a model trained of a large corpus of data
 Transformer model that accounts for the words but also the
context related to other words.
 teaches the computer to anticipate purposefully hidden text inside
examples of unannotated language
 RoBERTa outperforms BERT in terms of the masked language
modelling aim and performs better on subsequent tasks.

12. TESTING
Postive reviews and their Polarity Scores
Review – “I am so happy I orderd it”
Here the Comment is I am so happy I orderd it , as we can see the review is about
a satisfied customer who is
happy with this product, hence we can say this is a positive review.
Now if we see the polarity scores that are
Negative (neg) – 0.0
Here there is no negative words or emotion hence the neg score is 0
Positive(pos) – 0.517
The positive score here is good as the words suggest that the review is overall a
positive review.
Compound Score – 0.646

13. TESTING
Review – “This is bad I hated it”
Here the Comment isThis is bad I hated it, as we can see the review is about
unsatisfied customer who is
unhappy with this product, hence we can say this is a negative review.
Now if we see the polarity scores that are
Negative (neg) – 0.658
32
Here there is a customer who is unhappy with the product so the emotion here is
negative, Hence the negative
value will be higher here.
Positive(pos) – 0.0
Here there is no positive words or emotion hence the neg score is 0.
Compound Score – 0.8271

14. CONCLUSION
 As we can see in our model the ROBERta has scores that are much
confident than theVADER model as it used deep learning
approach. Due to this approach a difference can be seen between
the two models.
 One cannot dismiss the value that sentiment analysis offers to the
industry despite all the obstacles and potential issues that it faces.
Sentiment analysis is destined to become one of the key
determinants of many business decisions in the future because it
bases its findings on elements that are fundamentally
compassionate.
 Sentiment analysis’s results are helpful. It cannot be used to
forecast a company’s success or other measures. Sentiment
analysis may occasionally be unnecessary and only serve as a
reporting measure after the harm has already been done.

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16. ThankYou