Application-Of-NLP-Sentimental-Analysis.pptx

1. Application Of NLP Sentimental Analysis
Name : Keshav Bandil
Enrollment No: 0901AD221042
Course : Deep Learning
Presented To : Dr. Abhishek Bhatt

2. Sentiment Analysis
Sentiment analysis is a method of determining the emotional tone or attitude expressed in a piece of text, typically categorizing it as
positive, negative, or neutral.
Identify Emotional Tone
Determine whether text expresses positive, negative, or
neutral sentiments. This forms the bedrock of sentiment
analysis, enabling nuanced understanding of opinions.
Gauge Public Opinion
Understand the overall sentiment towards a brand, product,
or topic. This is crucial for market research and reputation
management.
Track Trends
Monitor how sentiment evolves over time, revealing shifts
in perception. Spot emerging patterns and adapt strategies
accordingly.
Support Decision-Making
Provide emotional insights that inform strategic choices.
Integrate sentiment data with business intelligence for
comprehensive analysis.

3. Types of Sentiment Analysis
Fine-Grained Sentiment
Analysis
This advanced approach measures
sentiment on a detailed scale, ranging
from "very positive" to ""very negative."
By capturing the nuances of emotions,
organizations gain a more accurate
assessment of public sentiment. .
Emotion Detection
Emotion detection goes a step further by
identifying specific emotions within the
text, such as joy, anger, sadness, and fear.
This allows for deeper insights into
customer satisfaction and brand
perception.
Aspect-Based Sentiment
Analysis (ABSA)
ABSA focuses on identifying sentiment
related to specific features or aspects of a
product or service.

4. How Sentiment Analysis Works
Data Collection
Gathering data from diverse sources, including social
media, surveys, and customer emails, forms the
foundation of sentiment analysis. The broader the range
of data sources, the more comprehensive the insights.
Text Preprocessing
Cleaning and preparing text data is essential.
Tokenization, stopword removal, and
stemming/lemmatization streamline the analysis by
reducing noise. These steps improve the accuracy and
efficiency of sentiment classification.
Sentiment Classification
Utilizing a range of approaches such as rule-based
methods, machine learning models, and deep learning
models, sentiment classification categorizes text. Each
method has its strengths, offering flexibility for various
applications.
Lexicon-Based Techniques
Sentiment dictionaries like VADER and SentiWordNet
assign predefined sentiment scores to words, allowing
quick and efficient sentiment classification. They help
determine overall sentiment by aggregating individual
word scores, improving both speed and accuracy.

5. Sentiment Analysis
1
Data Collection
Gathering data from diverse sources, including social media, surveys, and customer emails, forms the foundation of
sentiment analysis.
2
Text Preprocessing
Cleaning and preparing text data is essential. Tokenization, stopword removal, and stemming/lemmatization streamline the
analysis by reducing noise.
3
Sentiment Classification
Utilizing a range of approaches such as rule-based methods, machine learning models, and deep learning models, sentiment
classification categorizes text.
4
Lexicon-Based Techniques
Sentiment dictionaries like VADER and SentiWordNet, which are databases of words and their associated sentiment
scores, are used.

6. Sentiment Analysis Libraries and Resources
Python Libraries
TextBlob: Simplifies sentiment
analysis tasks with an easy-to-use
API.
NLTK: Offers extensive tools for
natural language processing,
including sentiment analysis.
SpaCy: Designed for advanced NLP
tasks, providing efficient and
accurate sentiment analysis
capabilities.
Pre-trained Models
BERT: Delivers cutting-edge
sentiment analysis performance
with its transformer-based
architecture.
RoBERTa: Builds upon BERT,
providing enhanced accuracy and
efficiency for sentiment
classification.
Commercial Tools
MonkeyLearn: Provides
customizable sentiment analysis
solutions for diverse business
needs.
IBM Watson: Offers advanced NLP
capabilities, including sentiment
analysis, through its cloud-based
platform.
Google Cloud NLP: Leverages
Google's AI expertise to provide
robust sentiment analysis tools.

7. Real-World Use Cases
Social Media
Monitoring
Track brand reputation
on platforms like
Twitter and Facebook,
identifying potential
crises. This allows
organizations to
address negative
feedback proactively
and leverage positive
sentiment for
marketing.
Customer
Feedback Analysis
Analyze reviews on
platforms like Amazon,
Yelp, and TripAdvisor
to gain insights into
customer satisfaction.
This helps companies
understand what
customers like and
dislike, enabling them
to improve products
and services.
Financial
Sentiment
Predict stock
movements from news
headlines. This helps
investors make
informed decisions
based on market
sentiment. By tracking
the emotional tone of
financial news,
investors can
anticipate market
trends.
Political
Sentiment
Analyze public opinion
during elections,
providing insights into
voter preferences. This
enables campaigns to
fine-tune their
messaging and target
specific demographics.

8. Evaluation Metrics and Future Trends
Core Metrics
Accuracy, Precision, Recall, and F1-Score are essential for gauging model performance.
Confusion Matrix
Visualizing true positives, true negatives, false positives, and false negatives.
ROC-AUC
Assessing the model's ability to discriminate between classes
effectively.
As sentiment analysis matures, expect greater integratin with emotion AI and enhanced real-time monitoring capabilities. Sentiment
analysis continues to evolve to adapt to the complexities of human expression.

9. Conclusion
Sentiment analysis connects data with human emotions, making business intelligence more insightful. As AI evolves, it will offer even
deeper emotional insights, helping companies improve customer engagement. In today9s digital world, using sentiment analysis isn9t just
beneficial. It9s essential for building meaningful customer relationships.

10. Thank You