Trying My First NLP Project: Learning Text Analysis with Python

The Day I Decided to Actually Build Something

Reading about NLP was interesting, but I kept feeling like something was missing. I could explain what sentiment analysis was, recite what tokenization meant, but could I actually build anything?

That question bothered me enough that I finally said: Okay, let's just try building something. Even if it's terrible.

So I picked the most common beginner NLP project I could find: a sentiment analyzer that determines if text is positive or negative. Simple enough, right?

Spoiler: It was harder than I expected. But also way more educational and fun than just reading tutorials.

Here's the story of my first real NLP project—the mistakes, the discoveries, and what I actually learned by trying.

Why Sentiment Analysis?

I chose sentiment analysis for a few reasons:

1. It's immediately understandable: You don't need to know NLP to understand this review is positive vs this review is negative.

2. There's clear success/failure: Unlike some AI projects where good is subjective, you can test your sentiment analyzer and immediately see if it's right or wrong.

3. It's actually useful: Companies use sentiment analysis to monitor brand reputation, analyze customer feedback, gauge public opinion—real applications!

4. Resources exist: As a popular beginner project, there are datasets, tutorials, and examples I could reference when stuck.

Plus, I was genuinely curious: How does a computer figure out that This movie was fantastic! is positive while What a waste of time is negative?

The Plan (What I Thought Would Happen)

In my head, building a sentiment analyzer would go like this:

1. Find a dataset of reviews labeled positive/negative
2. Feed it to some NLP library
3. Train a model
4. Test it
5. Done! I have a working sentiment analyzer.

Maybe an afternoon of work? Two days tops?

Yeah... let me tell you about what actually happened.

Step 1: Finding Data (Harder Than Expected)

First surprise: finding good data isn't trivial.

I searched for sentiment analysis dataset and got overwhelmed with options:

• Movie reviews (IMDB dataset)
• Product reviews (Amazon reviews)
• Twitter sentiment
• Restaurant reviews
• Financial news sentiment

I eventually settled on the IMDB movie reviews dataset because:

• It's widely used (plenty of help available)
• Already labeled as positive/negative
• Large enough to be meaningful but not huge
• Movie reviews are fun to read!

The dataset has 50,000 reviews: 25,000 for training and 25,000 for testing.

First Lesson: Data Exploration Matters

Before diving into code, I spent time reading the reviews. This seemed boring but turned out crucial. I noticed sarcasm, varied lengths, creative spelling, HTML tags, and all-caps reviews. Understanding your data before processing it? Apparently important!

Step 2: Text Preprocessing (The Unglamorous Part)

This is where I learned that most of NLP is data cleaning. Not sexy, but necessary.

Here's what I had to do to the raw reviews:

Removing HTML Tags

Some reviews had HTML in them:

import re
from html import unescape

def clean_html(text):
    # Remove HTML tags
    text = re.sub(r'<[^>]+>', '', text)
    # Decode HTML entities
    text = unescape(text)
    return text

# Example
raw = This movie was <br />amazing!&nbsp;Loved it!
clean = clean_html(raw)
print(clean)  # This movie was amazing! Loved it!

Lowercasing

Amazing and amazing should be treated the same:

text = text.lower()

Simple, but it helped consistency.

Removing Punctuation

This one I debated. Does punctuation matter?

Initially, I removed it all:

import string

def remove_punctuation(text):
    return text.translate(str.maketrans('', '', string.punctuation))

# Great movie! becomes Great movie

Later I realized: maybe exclamation marks indicate strong sentiment? This is something I'm still figuring out.

Tokenization (Breaking Text Into Words)

Splitting text into individual words:

from nltk.tokenize import word_tokenize

text = This movie was absolutely fantastic!
tokens = word_tokenize(text.lower())
print(tokens)
# ['this', 'movie', 'was', 'absolutely', 'fantastic', '!']

Removing Stop Words

Words like the, a, is appear everywhere but don't indicate sentiment:

from nltk.corpus import stopwords

stop_words = set(stopwords.words('english'))

def remove_stopwords(tokens):
    return [word for word in tokens if word not in stop_words]

# Before: ['this', 'movie', 'was', 'absolutely', 'fantastic']
# After: ['movie', 'absolutely', 'fantastic']

Stemming/Lemmatization

Converting words to their base form:

• running, runs, ran → run
• better, best → good

from nltk.stem import PorterStemmer

stemmer = PorterStemmer()
stemmed = [stemmer.stem(word) for word in tokens]

# movies → movi (not perfect, but consistent!)

My Complete Preprocessing Pipeline

After much trial and error, here's what I ended up with:

import re
import nltk
from html import unescape
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize

nltk.download('punkt')
nltk.download('stopwords')

def preprocess_text(text):
    # Clean HTML
    text = re.sub(r'<[^>]+>', '', text)
    text = unescape(text)
    
    # Lowercase
    text = text.lower()
    
    # Tokenize
    tokens = word_tokenize(text)
    
    # Remove stopwords and short words
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens 
              if word not in stop_words and len(word) > 2]
    
    # Stem
    stemmer = PorterStemmer()
    tokens = [stemmer.stem(word) for word in tokens]
    
    return ' '.join(tokens)

# Test it
review = This movie was absolutely fantastic! Best film I've seen this year!
processed = preprocess_text(review)
print(processed)
# Output: movi absolut fantast best film seen year

Is this perfect? Probably not. But it's a start!

Step 3: Converting Text to Numbers (The Magic Part)

Computers can't understand words directly. They need numbers.

Attempt 1: Bag of Words

The simplest approach: count how often each word appears.

from sklearn.feature_extraction.text import CountVectorizer

reviews = [
    great movie loved it,
    terrible movie waste of time,
    amazing film
]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)

print(vectorizer.get_feature_names_out())
# ['amazing', 'film', 'great', 'it', 'loved', 'movie', ...]

print(X.toarray())
# Each review becomes a vector of word counts

This worked okay but had a problem: the and and appear frequently but don't indicate sentiment.

Attempt 2: TF-IDF

TF-IDF (Term Frequency-Inverse Document Frequency) weights words by importance:

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(processed_reviews)

# Words that appear in every review get low scores
# Distinctive words get high scores

This felt smarter and actually performed better!

Step 4: Training a Model (The Scary Part)

With my text converted to numbers, I needed to train a classifier.

I started with the simplest option: Logistic Regression.

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, labels, test_size=0.2, random_state=42
)

# Train model
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2%}")

The moment I ran this and saw:

Accuracy: 87.23%

I literally said Wait, it works?! out loud.

87% accuracy on my first try felt amazing! Then I learned that's actually pretty standard for this dataset with basic methods. But still, I built something that worked!

Step 5: Testing With Real Examples (Reality Check)

Numbers are one thing, but does it actually work on real text?

def predict_sentiment(text):
    processed = preprocess_text(text)
    vectorized = vectorizer.transform([processed])
    prediction = model.predict(vectorized)[0]
    probability = model.predict_proba(vectorized)[0]
    
    sentiment = Positive if prediction == 1 else Negative
    confidence = max(probability) * 100
    
    return sentiment, confidence

# Test it!
test_reviews = [
    This movie was absolutely amazing!,
    Worst film I've ever seen. Complete waste of time.,
    It was okay, nothing special.,
    I laughed, I cried, masterpiece!,
]

for review in test_reviews:
    sentiment, confidence = predict_sentiment(review)
    print(f"Review: {review}")
    print(f"Sentiment: {sentiment} ({confidence:.1f}% confident)\n")

Results:

• This movie was absolutely amazing! → Positive (94.2% confident) ✓
• Worst film I've ever seen... → Negative (91.7% confident) ✓
• It was okay, nothing special. → Positive (54.3% confident) ✗ (Should be neutral/negative)
• I laughed, I cried, masterpiece! → Positive (88.5% confident) ✓

Three out of four! Not bad, but the neutral review confused it.

What Worked and What Didn't

What Worked:

1. TF-IDF was better than basic word counts: Weighing words by importance helped a lot.

2. Preprocessing mattered: Cleaning the text improved accuracy by about 5%.

3. Simple models work: Logistic Regression performed well without needing complex deep learning.

4. More training data helped: When I used the full 25,000 reviews vs just 5,000, accuracy jumped significantly.

What Didn't Work:

1. Handling sarcasm: Oh great, another explosion scene 🙄 was marked positive because of great.

2. Neutral sentiments: The model only knew positive/negative, so neutral reviews confused it.

3. Context: Not bad is positive, but not usually indicates negative sentiment.

4. Emojis and slang: This movie is 💯 wasn't recognized because I filtered emojis.

Mistakes I Made (So You Don't Have To)

Mistake 1: Not Exploring the Data First

I jumped straight into coding and missed obvious issues in the data that wasted time later.

Lesson: Always look at your data before processing it.

Mistake 2: Over-Preprocessing

At one point, I was removing so much text that reviews became meaningless. Balance is key.

Lesson: Each preprocessing step should have a reason. Test with and without to see the effect.

Mistake 3: Not Understanding the Model

I used Logistic Regression because a tutorial said so, without understanding why or what it does.

Lesson: Understand at least the basics of what your model does. It helps debug problems.

Mistake 4: Only Looking at Accuracy

87% accuracy sounds great! But I didn't look at which reviews it got wrong until later.

Lesson: Examine your errors. They teach you what your model struggles with.

Mistake 5: Giving Up When Things Broke

My code threw errors constantly at first. Import errors, shape mismatches, encoding issues.

Lesson: Errors are normal. Google them, read documentation, try again.

What I Learned About NLP

Data cleaning is 70% of the work - Not glamorous, but essential. There's no right answer - Test different approaches and see what works. Start simple - Basic models taught me fundamentals before jumping to complex ones. Context is hard - Teaching computers to understand context like humans is genuinely difficult. Failure teaches more - Wrong predictions revealed more about NLP challenges than successes.

What I'd Do Differently Next Time

If I started over:

1. Split data exploration into its own step: Don't rush into coding.

2. Create a validation set: I only used train/test, should have had train/validation/test.

3. Try multiple models: Compare Logistic Regression, Naive Bayes, Random Forest, etc.

4. Save intermediate results: I reran preprocessing constantly. Should have cached it.

5. Track experiments: I forgot which preprocessing steps led to which accuracy scores.

Tools That Helped Me

Libraries I Used:

• NLTK: For tokenization, stopwords, stemming
• scikit-learn: For vectorization and model training
• pandas: For data manipulation
• matplotlib: For visualizing results

Resources That Saved Me:

• Scikit-learn documentation: Surprisingly readable
• Stack Overflow: For every error I encountered
• Kaggle kernels: Seeing how others approached the same problem

The Most Satisfying Moments

1. First successful run: When my code finally ran without errors after two hours of debugging.

2. Seeing 87% accuracy: Realizing I built something that actually works.

3. Testing on my own sentences: Making up reviews and seeing if the model gets them right.

4. Understanding why it failed: Analyzing a misclassified review and realizing oh, that's why!

5. Explaining it to a friend: Being able to describe what I built and how it works.

What's Next for Me

This project opened up more questions:

• How do you handle sarcasm and irony?
• Can I detect neutral sentiment too?
• What about emojis and internet slang?
• How do you do this in multiple languages?
• What are these transformer models people keep mentioning?

I'm thinking about building:

• A tweet sentiment analyzer (shorter text, different challenges)
• A review summarizer (extracting key points)
• A topic classifier (categorizing text by subject)

Each project will teach me something new about NLP.

Advice for Your First NLP Project

If you're thinking about trying your own NLP project:

Start with a clear, simple goal: Classify reviews as positive/negative is better than build an AI that understands language.

Use existing datasets: Don't collect your own data for your first project. Use IMDB reviews, Amazon reviews, etc.

Don't worry about perfection: My 87% accuracy isn't state-of-the-art, but it taught me tons.

Read the errors: When your code breaks, the error message usually tells you what's wrong.

Test frequently: Don't write 100 lines and then run it. Write a bit, test it, repeat.

Have fun with it: Test your model on silly sentences. Make it try to analyze song lyrics or your own tweets.

The Bigger Picture

Building this sentiment analyzer taught me something important: NLP isn't magic, it's just clever problem-solving plus lots of data.

Every time I use Google Translate, ask Siri a question, or see YouTube captions now, I think about the work that went into making that possible. Someone (probably a team of someones) figured out how to break down that problem, clean the data, train a model, and make it work at scale.

My little sentiment analyzer that runs on my laptop and gets 87% accuracy? That's nothing compared to production systems. But it's a start. And it demystified how all of this works.

Final Thoughts

Is my sentiment analyzer perfect? No. Did I build something novel? No. Did I learn a ton and have fun doing it? Absolutely.

That's what first projects are for—learning by doing, making mistakes, and figuring things out.

If you're curious about NLP, stop reading and start building. Pick a simple project, find a dataset, and give it a try. You'll get stuck, you'll get frustrated, and you'll eventually get it working. And when you do, it feels great.

I'm already thinking about my next NLP project. The journey is just beginning, and I'm excited to see where curiosity and experimentation lead me next.

Built your first NLP project or thinking about it? I'd love to hear what you're working on! Connect with me on Twitter or LinkedIn and let's share our learning experiences.

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