11/21/2023 0 Comments Fake user data generator![]() ![]() The data contained features such as agent’s name, details about the order (order number, date, order type, items ordered, and total price), details about the call (date, duration, and the call text), and details about the caller (name, age, gender, and location). To simulate the use case, a sample dataset with at least 1000 rows was generated. Calculate and analyze the sentiment of the call text and tweets.Analysis of conversation between caller and agent.Create data to replicate actual customer use case using pandas.If a customer tweets about their experience, the sentiment of the tweet is calculated. The conversation also may include a threat to complain on social media about the caller’s concerns. Objectives also include gauging the sentiment of the conversation. The data production starts transcribing a call between a caller and an agent, where details about the call such as call date and time are recorded. The purpose of using fake data is to hide any sensitive data. This project’s use case is to simulate data similar to data from food delivery services (such as DoorDash, UberEats, etc). For this project, my objective was to generate data using a tool such as ”faker”. To calculate and analyze sentiments of text-based data in my prior projects, I retrieved data from public sources such as social media and Machine Learning repositories. I initially learned how to navigate, analyze and interpret data, which led me to generate and replicate a dataset. I am an intern currently learning data science. ![]()
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