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 necessary for market research, content creation, or moving forward in your field. In this tutorial, we offer a practical guide to building your direction tool using Python. Without the need for external applications programming or complex settings, you will learn how to detect web sites that can be accessed to the public, and the application of strong NLP (natural language processing) techniques such as feelings analysis and topic modeling, and depicting emerging trends using dynamic clouds.

import requests
from bs4 import BeautifulSoup


# List of URLs to scrape
urls = ["",
        ""]  


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 symbol excerpt above, we offer a direct way to rid the text data from web sites that can be accessed to the public using Python and Beutifulsoup requests. It brings content from the specific URL addresses, extracts paragraphs from HTML, and prepares them for more NLP analysis by combining text data in organized chains.

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))

Next, we clean the iconic text by converting it into small stones, removing punctuation and special letters, and filtering the common English stops using NLTK. This pre -processing ensures that the text data is clean, focused and ready for a 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 the words frequencies from the cleaned text data, and we define the best 10 main words. This helps to highlight the prevailing trends and frequent topics through the collected documents, providing immediate visions on popular or important topics within the broken 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 😀"
    elif polarity < -0.1:
        sentiment = "Negative 🙁"
    else:
        sentiment = "Neutral 😐"
    print(f"Document {i} Sentiment: {sentiment} (polarity={polarity:.2f})")

We perform feelings analysis on each cleaned text document using Textblob, Python Library built on top of NLTK. It establishes the general emotional tone of each document – positive, negative or neutral – and prints feelings along with a digital polar degree, providing a quick indication of the general mood or position within the text data.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation


# Adjust these parameters
vectorizer = CountVectorizer(max_df=1.0, min_df=1, stop_words="english")
doc_term_matrix = vectorizer.fit_transform(cleaned_texts)


# Fit LDA to find topics (for instance, 3 topics)
lda = LatentDirichletAllocation(n_components=3, random_state=42)
lda.fit(doc_term_matrix)


feature_names = vectorizer.get_feature_names_out()


for idx, topic in enumerate(lda.components_):
    print(f"Topic {idx + 1}: ", [vectorizer.get_feature_names_out()[i] for i in topic.argsort()[:-11:-1]])

Next, we apply Dirichlet Content Dirichlet (LDA) customization – a popular themesal algorithm – to discover basic topics in the text collection. It first converts the cleaned texts into a digital-term digital matrix using Countvesorizer from Scikit-Learn, then fits the LDA model to define basic topics. Outputs list the upper keywords for each subject discovered, and summarize the main concepts of the collected data.

# Assuming you have your text data stored in combined_text
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import nltk
from nltk.corpus import stopwords
import re


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


# Preprocess and clean the text:
cleaned_texts = []
for text in collected_texts:
    text = re.sub(r'[^A-Za-z\s]', ' ', text).lower()
    words = [w for w in text.split() if w not in stop_words]
    cleaned_texts.append(" ".join(words))


# Generate combined text
combined_text = " ".join(cleaned_texts)


# Generate the word cloud
wordcloud = WordCloud(width=800, height=400, background_color="white", colormap='viridis').generate(combined_text)


# Display the word cloud
plt.figure(figsize=(10, 6))  # <-- corrected numeric dimensions
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title("Word Cloud of Scraped Text", fontsize=16)
plt.show()

Finally, we create cloud perceptions of the word displaying major words of common and combined textual data. By visually emphasizing the most common and relevant terms, this approach allows the intuitive exploring of the main trends and topics in the collected web content.

The cloud output of the word from the broken site

In conclusion, we have successfully built a powerful trend. This exercise equipped you with a practical experience in the web scrolling, the NLP analysis, the modeling of the topic, and the intuitive perceptions using words of words. Through this strong and direct approach, you can constantly track the trends of industry, gain valuable visions of social content and blog, and make enlightened decisions based on data in actual time.

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Asif Razzaq is the CEO of Marktechpost Media Inc .. As a pioneer and vision engineer, ASIF is committed to harnessing the potential of artificial intelligence for social goodness. His last endeavor is to launch the artificial intelligence platform, Marktechpost, which highlights its in -depth coverage of machine learning and deep learning news, which is technically sound and can be easily understood by a wide audience. The platform is proud of more than 2 million monthly views, which shows its popularity among the masses.