Using Time Series Analysis, Sentiment Analysis, Tensorflow and SkLearn Extracting Insights from Walmart App Reviews
Browse filesIn today's digital age, understanding customer sentiments is paramount for any business aiming to thrive. Walmart, being a retail giant, recognizes the value of staying attuned to its customers' thoughts and emotions. In this dynamic data science project, we embarked on a journey to unlock the wealth of insights hidden within customer app reviews.
In this article we used app_store_scraper to scrap 50,000 customer review on App Store.
Objective:
To understand how user ratings evolved over time and identify any significant trends or patterns.
Utilizing time series forecasting techniques to predict future rating trends, enabling proactive decision-making.
Employing Natural Language Processing (NLP) techniques to extract sentiments from user comments, providing qualitative insights.
To provide valuable, data-driven recommendations for decision-makers based on the extracted insights from the app reviews.
Combining various statistical models (Statsmodels, Holt-Winters) and machine learning libraries (NLTK, TextBlob, TensorFlow, Scikit-learn) to conduct comprehensive analyses.
|
@@ -1,3 +1,5 @@
|
|
| 1 |
---
|
| 2 |
license: openrail
|
| 3 |
---
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: openrail
|
| 3 |
---
|
| 4 |
+
In today's digital age, understanding customer sentiments is paramount for any business aiming to thrive. Walmart, being a retail giant, recognizes the value of staying attuned to its customers' thoughts and emotions. In this dynamic data science project, we embarked on a journey to unlock the wealth of insights hidden within customer app reviews.
|
| 5 |
+
In this article we used app_store_scraper to scrap 50,000 customer review on App Store.
|