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Youtube Sentiment Analysis: Jake Paul vs. Mike Tyson Trailer

BeginnerGuided Project

Learn sentiment analysis with Python and data visualization techniques to evaluate YouTube comments on the Jake Paul vs. Mike Tyson trailer. This hands-on project guides you through data processing methods to classify sentiments and extract actionable insights using libraries like pandas, numpy, and nltk. Ideal for beginners in data science or marketing professionals, complete this engaging introduction to sentiment analysis in just 30 minutes and enhance your analytical skill set.

4.6 (99 Reviews)

Language

  • English

Topic

  • Data Visualization

Enrollment Count

  • 424

Skills You Will Learn

  • Python, Data Visualization, Pandas, Numpy, Data Analysis

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • May 19, 2025
About this Guided Project
Project Overview

Explore text sentiment analysis with python and unlock valuable insights from text data! This guided project focuses on evaluating sentiments in YouTube comments related to the Jake Paul vs. Mike Tyson trailer, using powerful Python libraries such as pandas, numpy, and nltk. Gain hands-on experience with essential data processing techniques to classify sentiments and extract actionable insights from textual data. Designed for beginners in data science and marketing professionals seeking to enhance analytical capabilities, this project equips you with valuable skills in just 30 minutes.



Learning Objectives

Upon completion of this project, you will be proficient in:
- Grasping the fundamental concepts of sentiment analysis and its practical applications.
- Employing Python libraries to effectively process text data for sentiment evaluation.
- Deriving significant insights from sentiment data to support decision-making across various scenarios.

Prerequisites

To complete this project successfully, ensure you have:
- A current version of a compatible web browser (Chrome, Edge, Firefox, Internet Explorer, or Safari) for optimal platform performance.


From emails and tweets to online survey responses, chats with customer service representatives and reviews, the sources available to gauge customer sentiment are seemingly endless. Sentiment analysis systems help companies better understand their customers, deliver stronger customer experiences and improve their brand reputation.

Instructors

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

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Contributors

Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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