If you love coffee and you are spending a long time looking for the perfect kind of beans, this repository for you
- The goal is to determine the overall sentiment of the reviews, whether they are positive, negative, or neutral.
- The outcome:
- understand customers and the perception of products.
- make data-driven decisions based on customer feedback.
- identify specific areas for improvement in coffee beans product or services.
- To use TextBlob, VADER, and SentiWordNet to assess the sentiment of coffee reviews.
- All three techniques, TextBlob, VADER, and SentiWordNet, are used for sentiment analysis of text data. They are commonly used in natural language processing to analyze the sentiment or emotional tone of a piece of text, such as a review or a social media post. Each technique takes a slightly different approach to sentiment analysis, but they all aim to identify and measure the polarity of the language used in a piece of text, whether it is positive, negative, or neutral.
- Compare three coffee bean brands: the first and third brands have more positive reviews than the second brand.
- Word Cloud of the three brands:
