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Analyzing Public Opinion with Python – Twitter Sentiment Analysis

Harness Machine Learning to Gain Insights into Public Sentiments, Discover Trends, and Monitor Your Brand’s Reputation.

  • Schedule

    28 – 30 March 2023

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

The text information over the internet is increasingly growing and sentiment analysis is now becoming a common tool to help companies, analysts, and researchers to learn public opinions towards certain topics by extracting positive and negative sentiments from each consisting word of a text document. Sentiment analysis is the process of using natural language processing, text analysis, and computational linguistics to identify and extract subjective information from text data.

Using data from scraping Twitter to do sentiment analysis involves collecting a large amount of textual data from Twitter, processing it using natural language processing techniques, and then analyzing the sentiment of the collected data. Using data from Twitter to perform sentiment analysis can be useful for a variety of applications, such as understanding public opinion on a particular topic, identifying emerging trends or issues, and monitoring brand reputation.

This 3-day online workshop is a beginner-friendly introduction to sentiment analysis using Machine Learning in Python and scraping tweets from Twitter. Throughout the online course, we will provide participants with hands-on examples and a rich interactive experience. One Instructor and two Teaching Assistants will help participants troubleshoot or help with any difficulties encountered.

Syllabus

  • Working with Conda Environment
  • Introduction to Python for data science
  • Data manipulation and processing with Python Pandas.
  • Understanding how to scrap
  • Finding the topic you want to scrap
  • Saving your data to csv
  • Cleaning text data
  • Understand the Practical and Theoretical Aspects of Naive Bayes,
  • Implementation of Naive Bayes for text classification

STUDENT TESTIMONIALS

This testimonial video is taken after our previous Online Data Science Series: Time Series Analysis for Business Forecasting.

LEARN FROM ANYWHERE

Our learning format is online-interactive, you will feel the interactive experience as if you were present in a physical classroom. You can access the class using your Zoom account on pre-defined dates.

  • LEARN AT YOUR OWN PACE

    Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.

  • PROOF YOUR MASTERY

    Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.

  • CONNECT WITH LIKE MINDED PEOPLE

    Be a part of our data-passionate community with 5000+ members and 1000+ alumni.

FOR ABSOLUTE BEGINNERS

Workshops in this series are tailored to casual programmers and non-programmers that are taking their first steps into data science. It assumes no prior knowledge or academic background, and attendees will be introduced to the beautiful art of writing R / Python code to produce data visualization and build machine learning models. The workshop has a gentle learning slope that is designed with non-technical professionals and academics in mind.

Yes, you can still attend the workshop as it is a beginner-friendly workshop.

Our system will send you an email containing a link and details to join a Google Classroom.

Online learning will be conducted via Zoom.us, Link to join the Zoom Class will be announced via Google Classroom.

Learning materials can be obtain via Google Classroom

Yes, you will receive a certificate of completion.

YOUR INSTRUCTOR

Handoyo Sjarif

A Senior Data Science Instructor at Algoritma Data Science School, he believes that accessible data science translates to numerous productivity benefits, leading to a better society. Handoyo is a passionate Instructor with expertise in both R and Python programming languages. He has more than 800 hours of teaching experience and involved in numerous consultative data science training for our clients, to name a few:

  • PT Sigma Metrasys Solution
  • PT. Indosat Ooredoo
  • Auto Insurance Fraud Analysis for Jasa Raharja
  • Bank Central Asia