Introduction
Sentiment analysis is one of the most popular applications of NLP and deep learning. It allows AI models to understand opinions, emotions, or attitudes expressed in text, making it invaluable for businesses, social media monitoring, and customer experience.
At curiositytech.in, learners in Nagpur start by building end-to-end sentiment analysis projects, progressing from data preprocessing to model deployment, gaining both theoretical understanding and practical experience.
1. What is Sentiment Analysis?
Sentiment Analysis (SA) is the process of classifying text into positive, negative, or neutral sentiment.
Core Steps in SA Projects:
- Text preprocessing
- Feature extraction
- Model building
- Evaluation and improvement
- Deployment
Real-World Applications:
- Monitoring brand reputation on social media
- Product reviews analysis
- Customer support automation
2. Data Collection and Preprocessing
Step 1 – Collect Data
- Use datasets like IMDB reviews, Twitter data, or Amazon product reviews.
Step 2 – Clean Text
- Lowercase conversion
- Remove punctuation, stopwords, URLs, and numbers
- Perform stemming or lemmatization
Step 3 – Tokenization
- Convert sentences into tokens or word sequences
- Example: “I loved the movie” → [I, loved, the, movie]
Step 4 – Padding Sequences
- Ensures all sequences have the same length for batch processing in neural networks
3. Feature Extraction
- Word Embeddings: Convert words into numerical vectors
- Word2Vec, GloVe → static embeddings
- BERT, Transformers → contextual embeddings
- Word2Vec, GloVe → static embeddings
- TF-IDF or Bag-of-Words: Simpler method for classical ML models
CuriosityTech Insight: Beginners often start with embeddings for deep learning projects, as they improve model understanding of semantic relationships.
4. Model Building
A simple LSTM-based sentiment classifier is ideal for beginners:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Sample data
reviews = [“I loved the movie”, “I hated the movie”]
labels = [1, 0]
# Tokenization
tokenizer = Tokenizer(num_words=1000)
tokenizer.fit_on_texts(reviews)
sequences = tokenizer.texts_to_sequences(reviews)
padded = pad_sequences(sequences, maxlen=5)
# Model
model = Sequential([
Embedding(input_dim=1000, output_dim=64, input_length=5),
LSTM(128),
Dense(1, activation=’sigmoid’)
])
model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])
model.fit(padded, labels, epochs=10)
Observation: Students at CuriosityTech see how accuracy improves after each epoch, providing instant feedback and motivation.
5. Model Evaluation
Metrics to Use:
- Accuracy
- Precision, Recall, F1-Score
- Confusion Matrix
Practical Tip: Visualizing the loss and accuracy curves helps beginners identify overfitting or underfitting, which is a critical skill for AI engineers.
6. Deployment and Real-World Integration
- Convert the model to TensorFlow SavedModel or TorchScript
- Integrate with web APIs using Flask or FastAPI
- Optionally deploy on cloud platforms like AWS AI, Google Vertex AI, or Azure Cognitive Services
CuriosityTech Example: Students deployed a sentiment analysis API to monitor live Twitter feeds, demonstrating practical industry-level experience.
7. Career Insight
- Sentiment analysis projects are often requested in AI engineer interviews.
- Mastery of text preprocessing, LSTM modeling, evaluation, and deployment demonstrates a complete skill set.
- At CuriosityTech.in, learners build portfolio-ready projects, increasing their chances of getting hired for NLP and AI roles.
8. Human Story
A beginner struggled with imbalanced classes in movie reviews. By applying class weighting and oversampling techniques, the model became accurate and robust. This hands-on experience reinforced the importance of practical problem-solving, not just coding.
Conclusion
Building a sentiment analysis model from scratch teaches learners how to handle sequential data, preprocess text, build LSTM networks, and deploy real-world AI solutions. Through guided mentorship at curiositytech.in, students gain career-ready skills, preparing them to work on NLP projects in industries ranging from finance to so
