NLP Chapter 4 – Sentiment Analysis in NLP | Opinion Mining with Machine Learning

Sentiment Analysis in Natural Language Processing

Sentiment analysis is one of the most popular and practical applications of Natural
Language Processing. It focuses on identifying the emotional tone or opinion expressed
in a piece of text.

Using sentiment analysis, machines can understand whether a sentence, review,
or comment is positive, negative, or neutral. This technique is widely used by
companies to analyze customer feedback and public opinion.

⭐ What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is the process of determining the
attitude or emotion behind text data. It helps businesses and organizations make
data-driven decisions based on user opinions.

📌 Types of Sentiment Analysis

• Binary Sentiment Analysis: Positive or Negative
• Multi-class Sentiment Analysis: Happy, Sad, Angry, Neutral
• Aspect-Based Sentiment Analysis: Opinion about specific features

📌 Approaches to Sentiment Analysis

1. Rule-Based Approach

This approach uses predefined sentiment lexicons where words are assigned
positive or negative scores.

2. Machine Learning Approach

Uses algorithms like Naive Bayes, Logistic Regression, or SVM trained on labeled data.

3. Deep Learning Approach

Uses neural networks such as RNNs, LSTMs, and Transformers to capture context
and complex language patterns.

📌 Example: Sentiment Analysis Using LSTM

from tensorflow import keras
from keras import layers

model = keras.Sequential([
    layers.Embedding(10000, 32),
    layers.LSTM(64),
    layers.Dense(1, activation='sigmoid')
])

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=['accuracy']
)

Model Output:

• 1 → Positive sentiment
• 0 → Negative sentiment

📌 Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-score

📌 Real-Life Applications

• Product review analysis
• Social media sentiment tracking
• Customer feedback systems
• Political and public opinion analysis

📌 Project Title

Movie and Product Review Sentiment Analyzer

📌 Project Description

In this project, you will build a sentiment analysis system that classifies reviews
as positive or negative. The system can be applied to movie reviews, product feedback,
or social media comments to extract valuable insights.

📌 Summary

Sentiment analysis enables machines to understand human emotions expressed in text.
By using machine learning and deep learning techniques, powerful systems can be built
to analyze opinions at scale. This chapter forms a critical bridge between basic NLP
concepts and real-world applications.