Scrape user feedback for patterns

Scraping user feedback for patterns involves collecting and analyzing comments, reviews, surveys, or support tickets to identify recurring themes, sentiments, or issues. Here’s a streamlined guide you can use:

1. Data Collection

• Sources: Gather feedback from emails, surveys, reviews (Google, Trustpilot, app stores), social media, support tickets, forums, etc.

• Tools: Use web scrapers (BeautifulSoup, Scrapy), APIs (Twitter, Trustpilot), or export tools (CSV from survey platforms).

• Storage: Organize the data into a structured format (CSV, database, or Excel) with fields like Date, User, Feedback Text, Rating, Category, etc.

2. Data Cleaning

• Remove duplicates.

• Normalize text (lowercasing, removing punctuation, stopwords).

• Tokenize and lemmatize for uniform analysis.

3. Pattern Detection

A. Keyword Frequency

• Use word clouds or frequency tables to find most mentioned words.

• Tools: nltk.FreqDist, collections.Counter, pandas.value_counts().

B. Topic Modeling

• Use LDA (Latent Dirichlet Allocation) to discover hidden topics in feedback.

• Example topics: “pricing”, “customer support”, “features”, “bugs”.

C. Sentiment Analysis

• Classify feedback as Positive, Neutral, Negative using:

  • Rule-based tools (TextBlob, VADER).

  • ML-based models (Transformers, fine-tuned BERT).

• Identify sentiment trends over time.

D. Clustering

• Use k-means or DBSCAN to group similar feedback.

• Helpful for identifying user segments or recurring issues.

E. N-gram Analysis

• Identify common phrases (e.g., “too expensive”, “crashes often”).

• Use bigrams/trigrams with scikit-learn or spaCy.

4. Actionable Insights

• Most Common Complaints: Extract frequently negative terms or topics.

• Feature Requests: Detect phrases like “wish it had”, “would like to see”.

• Positive Highlights: Recognize what users love to amplify in marketing.

• Urgent Fixes: Spot repeated mentions of bugs or critical failures.

5. Reporting

• Use dashboards (Tableau, Power BI, Google Data Studio) for stakeholders.

• Present with metrics: % positive/negative, top 5 issues, topic heatmaps.

• Highlight change over time if feedback is timestamped.

Example Patterns Found

• Support Response: “slow”, “no reply”, “rude” → Improve SLA.

• App Crashes: “freezes”, “bug”, “doesn’t load” → QA fix priority.

• Feature Gaps: “missing dark mode”, “no export option” → Roadmap update.

• Pricing Friction: “too costly”, “not worth it” → Review pricing strategy.

Let me know if you want a Python script or workflow for automating this process.