Sentiment Analysis Project — Artificial Intelligence

Introduction

Sentiment analysis is the computational task of identifying and classifying subjective opinions expressed in text. Whether analyzing product reviews, social media posts, or customer feedback, sentiment analysis transforms unstructured text into actionable intelligence. Companies use it to monitor brand perception, gauge customer satisfaction, and detect emerging issues before they escalate. This project builds a complete sentiment analysis system from data collection through deployment.

Problem Formulation

Input	Text string (review, tweet, comment)
Output	Sentiment class + confidence score
Binary	Positive / Negative
Ternary	Positive / Neutral / Negative
Fine-grained	Very Positive / Positive / Neutral / Negative / Very Negative
Aspect-based	"Battery life: Positive, Screen quality: Negative"
Input	"The camera is amazing but battery life is terrible"
Binary output	Positive (0.6 confidence) — but loses nuance!
Aspect-based	{camera: positive, battery: negative} — much better

Data Pipeline

Data Collection and Preprocessing

Step 1	Gather labeled data
Sources	IMDB reviews (50K), Amazon reviews (millions),
Step 2	Text preprocessing pipeline
Raw	"I LOVED this movie!!! Best film of 2024 🎬😍"
Lowercase	"i loved this movie!!! best film of 2024 🎬😍"
Remove emoji	"i loved this movie!!! best film of 2024"
Remove punct	"i loved this movie best film of 2024"
Tokenize	["i", "loved", "this", "movie", "best", "film", "of", "2024"]
Remove stops	["loved", "movie", "best", "film", "2024"]
Lemmatize	["love", "movie", "best", "film", "2024"]

Feature Engineering

Bag of Words (BoW)

Vocabulary: [love, hate, movie, terrible, amazing, ...]

"I love this amazing movie" → [1, 0, 1, 0, 1, ...]

TF-IDF (Term Frequency - Inverse Document Frequency)

TF(word, doc) = count(word in doc) / total_words_in_doc

IDF(word) = log(total_docs / docs_containing_word)

TF-IDF = TF × IDF

"movie" appears in 80% of reviews → low IDF (not discriminative)

"masterpiece" appears in 2% → high IDF (very discriminative)

Word Embeddings (Word2Vec/GloVe)

Each word → dense vector of 100-300 dimensions

"good" → [0.2, -0.1, 0.8, ...] (captures semantic meaning)

Document → average of word embeddings

Model Approaches

Classical Machine Learning

Naive Bayes Classifier

P(Positive | words) ∝ P(words | Positive) × P(Positive)

Training: Count word frequencies in positive vs negative reviews

P("amazing" | Positive) = 0.05

P("amazing" | Negative) = 0.001

P("terrible" | Positive) = 0.002

P("terrible" | Negative) = 0.04

Classification of "amazing movie terrible ending"

P(Pos) ∝ 0.05 × 0.02 × 0.002 × 0.01 × 0.5 = 1e-8

P(Neg) ∝ 0.001 × 0.02 × 0.04 × 0.01 × 0.5 = 4e-9

Result: Positive (higher) ✓

Logistic Regression with TF-IDF: Often 85-88% accuracy

SVM with TF-IDF: Often 87-90% accuracy

Deep Learning Approach

LSTM Sentiment Classifier

Input: "This movie was surprisingly good"

Embedding layer: Each word → 128-dim vector

LSTM processes sequence

h_1 = LSTM("this", h_0)

h_2 = LSTM("movie", h_1)

h_3 = LSTM("was", h_2)

h_4 = LSTM("surprisingly", h_3)

h_5 = LSTM("good", h_4) ← final hidden state

Dense(64, ReLU) → Dense(1, sigmoid) → probability of positive

The LSTM captures that "surprisingly good" is positive,

while "not good" would be negative (handles negation).

Transformer-Based (BERT)

2. Add classification head: BERT [CLS] output	Dense → Softmax
Result	93-95% accuracy on most benchmarks
BERT understands context: "The movie is not bad"	Positive

Handling Challenges

Negation: "I don't think this is good"	Negative
Solution	LSTM/BERT captures sequential context
Sarcasm	"Oh great, another meeting that could have been an email"
Solution	Very difficult! Requires world knowledge and tone
Mixed sentiment	"Great food but terrible service"
Solution	Aspect-based sentiment analysis
Domain shift	Model trained on movies, tested on electronics
Solution	Domain adaptation, few-shot fine-tuning

Evaluation

Example

Confusion Matrix for binary sentiment: Predicted Pos Neg Actual Pos [ TP=450 FN=50 ] Recall = 450/500 = 0.90 Neg [ FP=30 TN=470 ] Precision = 450/480 = 0.94 Accuracy = (450+470)/1000 = 0.92 F1-Score = 2 × (0.94 × 0.90) / (0.94 + 0.90) = 0.92

Deployment Architecture

4. Return	{sentiment: "positive", confidence: 0.94}
Monitoring	Track prediction distribution drift
Retraining	Monthly with new labeled data

Summary

Sentiment analysis demonstrates the full ML pipeline from data collection through deployment. Starting with simple Naive Bayes on bag-of-words features provides a strong baseline. Deep learning with LSTMs captures sequential patterns and context. Fine-tuned Transformers like BERT achieve state-of-the-art accuracy by leveraging pre-trained language understanding. The choice depends on your data volume, latency requirements, and accuracy needs.

Introduction

Problem Formulation

Input	Text string (review, tweet, comment)
Output	Sentiment class + confidence score
Binary	Positive / Negative
Ternary	Positive / Neutral / Negative
Fine-grained	Very Positive / Positive / Neutral / Negative / Very Negative
Aspect-based	"Battery life: Positive, Screen quality: Negative"
Input	"The camera is amazing but battery life is terrible"
Binary output	Positive (0.6 confidence) — but loses nuance!
Aspect-based	{camera: positive, battery: negative} — much better

Data Pipeline

Data Collection and Preprocessing

Step 1	Gather labeled data
Sources	IMDB reviews (50K), Amazon reviews (millions),
Step 2	Text preprocessing pipeline
Raw	"I LOVED this movie!!! Best film of 2024 🎬😍"
Lowercase	"i loved this movie!!! best film of 2024 🎬😍"
Remove emoji	"i loved this movie!!! best film of 2024"
Remove punct	"i loved this movie best film of 2024"
Tokenize	["i", "loved", "this", "movie", "best", "film", "of", "2024"]
Remove stops	["loved", "movie", "best", "film", "2024"]
Lemmatize	["love", "movie", "best", "film", "2024"]

Feature Engineering

Bag of Words (BoW)

Vocabulary: [love, hate, movie, terrible, amazing, ...]

"I love this amazing movie" → [1, 0, 1, 0, 1, ...]

TF-IDF (Term Frequency - Inverse Document Frequency)

TF(word, doc) = count(word in doc) / total_words_in_doc

IDF(word) = log(total_docs / docs_containing_word)

TF-IDF = TF × IDF

"movie" appears in 80% of reviews → low IDF (not discriminative)

"masterpiece" appears in 2% → high IDF (very discriminative)

Word Embeddings (Word2Vec/GloVe)

Each word → dense vector of 100-300 dimensions

"good" → [0.2, -0.1, 0.8, ...] (captures semantic meaning)

Document → average of word embeddings

Model Approaches

Classical Machine Learning

Naive Bayes Classifier

P(Positive | words) ∝ P(words | Positive) × P(Positive)

Training: Count word frequencies in positive vs negative reviews

P("amazing" | Positive) = 0.05

P("amazing" | Negative) = 0.001

P("terrible" | Positive) = 0.002

P("terrible" | Negative) = 0.04

Classification of "amazing movie terrible ending"

P(Pos) ∝ 0.05 × 0.02 × 0.002 × 0.01 × 0.5 = 1e-8

P(Neg) ∝ 0.001 × 0.02 × 0.04 × 0.01 × 0.5 = 4e-9

Result: Positive (higher) ✓

Logistic Regression with TF-IDF: Often 85-88% accuracy

SVM with TF-IDF: Often 87-90% accuracy

Deep Learning Approach

LSTM Sentiment Classifier

Input: "This movie was surprisingly good"

Embedding layer: Each word → 128-dim vector

LSTM processes sequence

h_1 = LSTM("this", h_0)

h_2 = LSTM("movie", h_1)

h_3 = LSTM("was", h_2)

h_4 = LSTM("surprisingly", h_3)

h_5 = LSTM("good", h_4) ← final hidden state

Dense(64, ReLU) → Dense(1, sigmoid) → probability of positive

The LSTM captures that "surprisingly good" is positive,

while "not good" would be negative (handles negation).

Transformer-Based (BERT)

2. Add classification head: BERT [CLS] output	Dense → Softmax
Result	93-95% accuracy on most benchmarks
BERT understands context: "The movie is not bad"	Positive

Handling Challenges

Negation: "I don't think this is good"	Negative
Solution	LSTM/BERT captures sequential context
Sarcasm	"Oh great, another meeting that could have been an email"
Solution	Very difficult! Requires world knowledge and tone
Mixed sentiment	"Great food but terrible service"
Solution	Aspect-based sentiment analysis
Domain shift	Model trained on movies, tested on electronics
Solution	Domain adaptation, few-shot fine-tuning

Evaluation

Example

Deployment Architecture

4. Return	{sentiment: "positive", confidence: 0.94}
Monitoring	Track prediction distribution drift
Retraining	Monthly with new labeled data

Introduction

Problem Formulation

Data Pipeline

Data Collection and Preprocessing

Feature Engineering

Bag of Words (BoW)

TF-IDF (Term Frequency - Inverse Document Frequency)

Word Embeddings (Word2Vec/GloVe)

Model Approaches

Classical Machine Learning

Naive Bayes Classifier

Classification of "amazing movie terrible ending"

Deep Learning Approach

LSTM Sentiment Classifier

LSTM processes sequence

Transformer-Based (BERT)

Handling Challenges

Evaluation

Deployment Architecture

Summary

Continue learning this concept

Introduction

Problem Formulation

Data Pipeline

Data Collection and Preprocessing

Feature Engineering

Bag of Words (BoW)

TF-IDF (Term Frequency - Inverse Document Frequency)

Word Embeddings (Word2Vec/GloVe)

Model Approaches

Classical Machine Learning

Naive Bayes Classifier

Classification of "amazing movie terrible ending"

Deep Learning Approach

LSTM Sentiment Classifier

LSTM processes sequence

Transformer-Based (BERT)

Handling Challenges

Evaluation

Deployment Architecture

Summary

Continue learning this concept