Review sentiment analysis is a process of automatically determining the sentiment expressed in a piece of text, typically in the form of a review or feedback. It involves using natural language processing (NLP) and machine learning techniques to analyze the language used in the text and classify it as having positive, negative, neutral or mixed emotions. The goal of review sentiment analysis is to understand the overall sentiment or opinion conveyed in the review, which can help businesses gauge customer satisfaction, identify areas for improvement, and make data-driven decisions.

Review classification is the process of categorizing reviews or feedback into different predefined categories based on their content or topic. It involves using natural language processing (NLP) and machine learning techniques to automatically classify reviews into categories specific to a certain industry, like service quality, cleanliness, or pricing. The goal of review classification is to organize large volumes of reviews or feedback efficiently, enabling businesses to identify common themes, prioritize areas for improvement, and make informed decisions to enhance customer satisfaction and overall performance.

NPS is a widely utilized metric in service industries to gauge customer loyalty and satisfaction. It is derived from a specific question asking customers about their likelihood to recommend a product or service, rated on a scale of 0-10. The responses are categorized into three groups:

Customers who provide a score of 6 or lower. They might actively dissuade potential customers from engaging with your business.Customers responding with a 7 or 8. They neither actively recommend nor discourage others from interacting with your business. These participants are excluded from the NPS calculation.Customers responding with a 9 or 10. These individuals represent your most enthusiastic and loyal customers, likely to enhance your business's reputation.

In our app, we offer three designated types of questions—age, sex, and country— that serve as powerful filters for organizing and analyzing survey data. These demographic questions allow users to segment survey responses based on key demographic attributes, enabling them to gain deeper insights into the preferences, behaviors, and perceptions of specific demographic groups. By leveraging age, sex, and country filters, users can tailor their data analysis to target audience segments, identify trends, and make data-driven decisions that drive meaningful outcomes. Whether analyzing customer satisfaction, market trends, or employee feedback, these demographic filters empower users to extract actionable insights and unlock the full potential of their survey data.