Oculus: Sentiment Analysis and Text Analysis
Let's explore each part of my project! Check out my GitHub Repository to see all my code.
Part 1: Data scraping and cleaning
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Web Scraping tools are specifically developed for extracting information from websites. It is useful to find your wanted data and analyze the data to make the right decision.
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I scraped the reviews from the bestbuy.com oculus review page: https://www.bestbuy.com/site/reviews/oculus-quest-2-advanced-all-in-one-virtual-reality-headset-256gb/6473857?variant=A​ using Selenium in Python and exported the data to an Excel file.
Part 2: Sentiment analysis
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Sentiment analysis is a powerful marketing tool that enables product managers to understand customer emotions in their marketing campaigns. It is an important factor when it comes to product and brand recognition, customer loyalty, customer satisfaction, advertising and promotion's success, and product acceptance.
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In my project, I predicted the review sentiment based off the review text. The model thus can be implemented to reviews with no rating or any writing comment content.​
Part 3: Text analysis
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Text analysis, also known as text mining, is the process of automatically classifying and extracting meaningful information from unstructured text.
Through text analysis, I categorized that customer like and dislike and analysed the most significant issues and clarify them. Then, the product manager know which part of the product does the best or unfortunately the worst by mentions in reviews.