Master VisiLearn's code playground to write, run, and experiment with NLP code directly in your browser.
No installation required. Write and run Python code directly in your browser with full NLP library support.
NLTK, spaCy, scikit-learn, pandas, numpy, and other popular NLP libraries are ready to use.
Advanced code editor with syntax highlighting, auto-completion, and error detection.
See your code results instantly with interactive output display and error messages.
Save your code snippets and share them with the community or export for external use.
Access a curated collection of NLP examples and tutorials to jumpstart your learning.
Navigate to the Code Playground from the main navigation or dashboard.
Start with an example from the gallery or write your own Python code in the editor.
Click the "Run" button to execute your code and see the results in the output panel.
Save your work to your account and share interesting examples with the community.
Clean and prepare text data for analysis
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
# Download required data
nltk.download('punkt')
nltk.download('stopwords')
text = "Natural Language Processing is fascinating!"
tokens = word_tokenize(text.lower())
stop_words = set(stopwords.words('english'))
filtered_tokens = [w for w in tokens if w not in stop_words]
print("Original:", text)
print("Tokens:", tokens)
print("Filtered:", filtered_tokens)Analyze the sentiment of text using VADER
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
texts = [
"I love this product! It's amazing!",
"This is okay, nothing special.",
"I hate waiting in long queues."
]
for text in texts:
scores = analyzer.polarity_scores(text)
print(f"Text: {text}")
print(f"Sentiment: {scores}")
print("---")Extract entities using spaCy
import spacy
# Load English language model
nlp = spacy.load("en_core_web_sm")
text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
doc = nlp(text)
print("Named Entities:")
for ent in doc.ents:
print(f"{ent.text} - {ent.label_} ({ent.start_char}-{ent.end_char})")Use the example gallery to understand common NLP patterns and techniques before writing your own code.
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