Data Collection Process

Music streaming platforms contain valuable user feedback. We colleect this data using Python web scraping techniques. The following example demonstrtaes fetching comments from a music platform:

import requests
from bs4 import BeautifulSoup

def get_song_comments(track_id):
    api_endpoint = f"https://api.music-service.com/tracks/{track_id}/comments"
    response = requests.get(api_endpoint)
    comment_data = response.json()
    return [item['text'] for item in comment_data['items']]

song_comments = get_song_comments('TRACK123')  # Example track ID

Data Processing

After collection, we clean and structure the data using Pandas:

import pandas as pd

comments_df = pd.DataFrame({'content': song_comments})
comments_df = comments_df.drop_duplicates().dropna()
comments_df['length'] = comments_df['content'].str.len()

Visualization Techniques

We generate insights through text analysis and visualization:

from wordcloud import WordCloud
import matplotlib.pyplot as plt

combined_text = ' '.join(comments_df['content'])
word_cloud = WordCloud(width=1200, height=600).generate(combined_text)

plt.figure(figsize=(12, 6))
plt.imshow(word_cloud)
plt.axis('off')
plt.show()

Django Integration

The analysis results are presented through a Django web application:

# views.py
from django.shortcuts import render
from analysis.models import SongAnalysis

def show_analysis(request, track_id):
    analysis = SongAnalysis.objects.get(track_id=track_id)
    return render(request, 'results.html', {'analysis': analysis})

<!-- results.html -->
<div class="analysis-container">
    <h2>Analysis for Track {{ analysis.track_id }}</h2>
    <div class="visualization">
        <img src="{{ analysis.cloud_image.url }}" alt="Comment Word Cloud">
    </div>
</div>