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Sentiment Analysis Tutorial

Goal

Classify text as positive/negative sentiment.

This is a perfect first NLP project because:

  • preprocessing matters
  • classic models work well

A baseline approach

TF-IDF features + Logistic Regression.

false

  flowchart LR
  T[Text] --> V[TF-IDF]
  V --> C[Classifier]
  C --> S[Sentiment]

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Example code (template)

Sentiment model (TF-IDF + LogisticRegression)
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
 
texts = [
    "I love this product",
    "This is terrible",
    "Amazing quality",
    "Worst purchase ever",
]
labels = [1, 0, 1, 0]
 
X_train, X_test, y_train, y_test = train_test_split(
    texts, labels, test_size=0.25, random_state=42, stratify=labels
)
 
model = Pipeline(
    steps=[
        ("tfidf", TfidfVectorizer(ngram_range=(1, 2))),
        ("clf", LogisticRegression(max_iter=1000)),
    ]
)
 
model.fit(X_train, y_train)
 
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
Sentiment model (TF-IDF + LogisticRegression)
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
 
texts = [
    "I love this product",
    "This is terrible",
    "Amazing quality",
    "Worst purchase ever",
]
labels = [1, 0, 1, 0]
 
X_train, X_test, y_train, y_test = train_test_split(
    texts, labels, test_size=0.25, random_state=42, stratify=labels
)
 
model = Pipeline(
    steps=[
        ("tfidf", TfidfVectorizer(ngram_range=(1, 2))),
        ("clf", LogisticRegression(max_iter=1000)),
    ]
)
 
model.fit(X_train, y_train)
 
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

Practical tips

  • include bigrams (ngram_range=(1,2)ngram_range=(1,2)) to capture phrases like “not good”
  • avoid removing negations like “not”
  • check class balance

Mini-checkpoint

Why is “not good” tricky for BoW?

  • without bigrams, “not” and “good” may cancel signals incorrectly.

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