A Step by Step Guide to Build a Trend Finder Tool with Python: Web Scraping, NLP (Sentiment Analysis & Topic Modeling), and Word Cloud Visualization
Monitoring and extracting trends from web content has become essential for market research, content creation, or staying ahead in your field. In this tutorial, we provide a practical guide to building your trend-finding tool using Python. Without needing external APIs or complex setups, you’ll learn how to scrape publicly accessible websites, apply powerful NLP (Natural […] The post A Step by Step Guide to Build a Trend Finder Tool with Python: Web Scraping, NLP (Sentiment Analysis & Topic Modeling), and Word Cloud Visualization appeared first on MarkTechPost.
Monitoring and extracting trends from web content has become essential for market research, content creation, or staying ahead in your field. In this tutorial, we provide a practical guide to building your trend-finding tool using Python. Without needing external APIs or complex setups, you’ll learn how to scrape publicly accessible websites, apply powerful NLP (Natural Language Processing) techniques like sentiment analysis and topic modeling, and visualize emerging trends using dynamic word clouds.
import requests
from bs4 import BeautifulSoup
# List of URLs to scrape
urls = ["https://en.wikipedia.org/wiki/Natural_language_processing",
"https://en.wikipedia.org/wiki/Machine_learning"]
collected_texts = [] # to store text from each page
for url in urls:
response = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
# Extract all paragraph text
paragraphs = [p.get_text() for p in soup.find_all('p')]
page_text = " ".join(paragraphs)
collected_texts.append(page_text.strip())
else:
print(f"Failed to retrieve {url}")First with the above code snippet, we demonstrate a straightforward way to scrape textual data from publicly accessible websites using Python’s requests and BeautifulSoup. It fetches content from specified URLs, extracts paragraphs from the HTML, and prepares them for further NLP analysis by combining text data into structured strings.
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
cleaned_texts = []
for text in collected_texts:
# Remove non-alphabetical characters and lower the text
text = re.sub(r'[^A-Za-z\s]', ' ', text).lower()
# Remove stopwords
words = [w for w in text.split() if w not in stop_words]
cleaned_texts.append(" ".join(words))Then, we clean the scraped text by converting it to lowercase, removing punctuation and special characters, and filtering out common English stopwords using NLTK. This preprocessing ensures the text data is clean, focused, and ready for meaningful NLP analysis.
from collections import Counter
# Combine all texts into one if analyzing overall trends:
all_text = " ".join(cleaned_texts)
word_counts = Counter(all_text.split())
common_words = word_counts.most_common(10) # top 10 frequent words
print("Top 10 keywords:", common_words)Now, we calculate word frequencies from the cleaned textual data, identifying the top 10 most frequent keywords. This helps highlight dominant trends and recurring themes across the collected documents, providing immediate insights into popular or significant topics within the scraped content.
!pip install textblob
from textblob import TextBlob
for i, text in enumerate(cleaned_texts, 1):
polarity = TextBlob(text).sentiment.polarity
if polarity > 0.1:
sentiment = "Positive 
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