Sentiment analysis is the process of analyzing digital text to determine if the emotional tone of the message is positive, negative, or neutral. Today, companies have large volumes of text data like emails, customer support chat transcripts, social media comments, and reviews. Sentiment analysis tools can scan this text to automatically determine the author's attitude towards a topic. Companies use the insights from sentiment analysis to improve customer service and increase brand reputation.

1. Welcome to our Presentation

2. Submitted To
Shah Md. Tanvir Siddiquee
Lecturer
Department of Computer Science Engineering
Daffodil International University

3. Submitted By
Ariful Islam
193-15-2969
Alifa Akhi
201-15-3639
Team

4. Outline
Introduction
Need of Sentiment Analysis
Application
Approch
Implementation
Relevent Data Set
Advantages
Conclusion

5. Sentiment analysis tries to uncover emotions
in the text. By analyzing movie reviews,
customer feedback, support tickets,
companies may discover many interesting
things. So learning how to build sentiment
analysis models is quite a practical skill.
For the accurate classification of sentiments,
many researchers have made efforts to
combine deep learning and machine
learning concepts in recent years. This
section briefly describes the numerous
studies related to sentiment analysis of web
contents about users' opinions, emotions,
reviews toward different matters and
products using deep learning techniques.
Introduction

6. Sentiments are feelings, opinions, emotions,
like/dislikes, good/bad
Sentiment Analysis is a Natural Languages Processing
and Information Extraction task that aims to obtain
writer’s feelings expressed in positive or negative
comments, questions and requests, by analyzing a
large numbers of documents.
Sentiment Analysis is a study of human behavior
in which we extract user opinion and emotion
from plain data or text
What is
Sentiment
Analysis
Sentiment Analysis is also known
as Opinion Mining

7. Example
User’s Opinion
Person 1 : It’s a good product. (Positive Statement)
Person 2 : Nah! I didn’t like it at all. (Negative
Statement)
Person 3: The new Infinity T-shirt is awesome!
(Positive statement)
Polarity
Positive
Negative
Neutral

8. Need of Sentiment Analysis
Rapid growth of
available
subjective text
on the internet
Web 2.0
To make
decision
Future Online
Shops

9. Application
Business &
Organization
INDIVIDUALS
A . Brand Analysis
B . New Product
perception
C . Product & Service
benchmark
A . Purchasing a
product or using a
service
B . Finding best
option in sports,
movies, etc.

10. NLP MACHINE LEARNING
A . Use semantic to understand the
language
B . Use SentiwordNet
A . Don’t have to understand the meaning
B . User classifiers such as Naïve Byes,
SVM, Linear Regression, Count Vectorizer
etc.
m o r e m o r e
Application Cont.

11. Data Sets

12. III
Working Procedure
Data
Tokenization
Sentence Splitter
Adjective Extraction
SentiWordNet Interpretation
Aggregating Scores

13. Data Flow Diagram
(DFD)

14. Advantages
A lower cost than traditional methods of
getting customer insight.
A faster way of getting insight from customer
data.
As 80% of all data in business consists of
words, the Sentiment Engine is an essential
tool for making sense of it all.
More accurate and insightfull customer
perceptions and feedback.

15. Conclusion ‘‘We have seen that Sentiment Analysis
can be used for analyzing opinions in
Product Reviews where a third person
narrates his views. We also studied NLP
and Machine Learning approaches for
Sentiment Analysis. Sentiment analysis
has Strong commercial interest because
Companies want to know how their
products are being perceived and also
Prospective consumers want to know their
existing user think.
Business Plans, Marketing Plans, Project
Proposals, Lessons, etc

16. Thank You
See You Next Time