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Scraped Mobile App Review Analysis 📱🔍

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Data Science Upskill Nlp Scraping
Junnielle Violanda
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Junnielle Violanda
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Table of Contents

Scraped Mobile App Review Analysis using NLP: Unveiling Insights from User Feedback 📱🔍
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Introduction 🚀
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In the era of digitalization, companies swim in a sea of customer feedback—social media posts, call logs, and mobile app reviews. Extracting actionable insights from this qualitative data is essential for any business with an online presence. Enter Natural Language Processing (NLP) and Machine Learning (ML). In this project blog post, I’ll guide you through building an NLP pipeline to analyze scraped mobile app reviews. Our use case? The Netflix mobile app.

Data Preparation 📊
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I started by collecting a dataset of 5000 recent reviews from the Netflix mobile app on Google Play. The daily review count graph gave me an idea of the data volume we’re dealing with. Most reviews are short to medium in length (under 50 words). Longer reviews were trimmed to a maximum of 48 words.

Data Preprocessing 🧹
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Text preprocessing is crucial for NLP. Here’s what I did:

  1. Clean Text: Remove URLs, emails, phone numbers, punctuations, tags, emojis, symbols, and pictographs.
  2. Stop Words Removal: Get rid of common words like “the,” “and,” and “is.”
  3. Lowercasing and Lemmatization: Standardize text by converting everything to lowercase and reducing words to their base form.
  4. Duplicates Removal: Eliminate identical reviews.
  5. Spell Checking: Fix typos.
  6. Non-English Reviews Removal: Keep only English reviews.

Topic Modeling with Latent Dirichlet Allocation (LDA) 🗂️
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Topic modeling helps us discover clusters of related words in text data. LDA is our tool of choice. Here’s how it works:

  1. Specify the number of topics.
  2. Let the algorithm find the topics without prior knowledge.
  3. Each topic is a cluster of words with associated probabilities.

Results and Insights 🌟
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After applying LDA, we unearthed topics from Netflix reviews:

  1. Content Quality: Discussions about movie choices, episodes, and boredom.
  2. User Experience: Comments on app navigation, interface, and usability.
  3. Technical Issues: Reports of bugs, crashes, and playback problems.
  4. Subscription Plans: Opinions on pricing, plans, and value for money.

Conclusion 🎉
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NLP empowers us to understand what users are saying beyond mere ratings. Armed with these insights, businesses can enhance their apps, improve user experience, and stay ahead in the digital game. So grab your dataset, preprocess it, and let NLP reveal the hidden gems in user reviews! 🌐📱

P.S. If you want to explore more NLP projects, check out GitHub or Appbot. 🤖.

Source: Conversation with Bing, 4/12/2024 (1) Customer Reviews Analysis using NLP — The Netflix Use Case. https://towardsdatascience.com/customer-reviews-analysis-using-nlp-the-netflix-use-case-92b3645770e1. (2) manojps/google-play-app-review-analysis - GitHub. https://github.com/manojps/google-play-app-review-analysis. (3) Opinion mining for app reviews: an analysis of textual representation …. https://link.springer.com/article/10.1007/s10515-021-00301-1. (4) NLP app reviews analysis tool for iOS & Google Play - Appbot. https://appbot.co/features/natural-language-processing/. (5) Create Dataset for Sentiment Analysis by Scraping Google Play App …. https://curiousily.com/posts/create-dataset-for-sentiment-analysis-by-scraping-google-play-app-reviews-using-python/.