Common NLP Tasks and Sentiment Analysis

NLP includes several tasks, each designed to solve a different language-processing problem.

Major NLP Tasks

• Text classification: Assigns categories to text, such as spam or non-spam.
• Sentiment analysis: Identifies expressed opinions or emotional polarity, such as positive, negative, or neutral.
• Named Entity Recognition (NER): Identifies entities such as people, organizations, locations, and dates.
• Machine translation: Converts text from one language into another.
• Text summarization: Produces a shorter version of a document while retaining important information.
• Question answering: Generates or retrieves answers from a question and relevant context.

Implementing a Simple Sentiment Classifier

A traditional machine learning model can classify text using TF-IDF features.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
texts = [
"The product is excellent",
"I love this service",
"The experience was wonderful",
"The product is terrible",
"I hate this service",
"The experience was disappointing"
]
labels = [
"positive",
"positive",
"positive",
"negative",
"negative",
"negative"
]
model = make_pipeline(
TfidfVectorizer(),
LogisticRegression()
)
model.fit(texts, labels)
new_texts = [
"I love this product",
"The service was terrible"
]
predictions = model.predict(new_texts)
for text, prediction in zip(new_texts, predictions):
print(text, "->", prediction)

This example demonstrates the workflow, not production-level sentiment understanding. The dataset is very small, so predictions may be unreliable for unfamiliar words, sarcasm, mixed opinions, or different writing styles.

Real-world systems require larger, representative datasets and appropriate evaluation.