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Artificial Intelligence in Social Media Analytics

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Artificial Intelligence in Social Media Analytics

This course is part of Social Media Analytics Specialization

Instructor: Ian McCulloh

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Learn to define and evaluate machine learning classifiers for effective data analysis.
Gain hands-on experience in processing and parsing social media text data using NLP techniques.
Explore methodologies for conducting sentiment analysis on social media content to gauge public opinion.
Master techniques for topic modeling, enabling the extraction of themes from social media conversations.

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

91% of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Social Media Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

In the course "Artificial Intelligence in Social Media Analytics", learners will explore the intersection of artificial intelligence and social media analytics, equipping them with essential skills to navigate and analyze digital landscapes. By delving into machine learning fundamentals, natural language processing, sentiment analysis, and topic modeling, participants will gain practical experience in applying AI techniques to real-world social media data. This course stands out by providing not only theoretical insights but also hands-on opportunities to construct classifiers, perform sentiment analysis, and build semantic networks, all tailored to the complexities of social media content.

As learners progress, they will develop a keen understanding of how AI can uncover hidden patterns, sentiment, and topics within vast amounts of unstructured data. The unique blend of foundational concepts and practical applications ensures that participants can effectively analyze social media interactions and derive actionable insights. Whether for career advancement or personal interest, this course offers a comprehensive toolkit to leverage AI for understanding social dynamics and enhancing engagement strategies in digital platforms.

Module details

This course introduces the fundamentals of machine learning and its application to social media content analysis. You will learn to evaluate classifiers, perform text processing and sentiment analysis, and implement topic modeling techniques. By the end, you will be equipped to build semantic networks and address challenges in natural language processing.

What's included

1 reading1 plugin

In this module, you will explore the fundamentals of machine learning (ML) from theory to application. You will also be able to define ML and learn to assess its performance. Additionally, you will gain practical experience constructing and evaluating ML classifiers. You will be able to compare the effectiveness of various ML models like Decision Trees, understanding their role in operationalizing data and the importance of data normalization in achieving optimal results.

What's included

5 videos3 readings3 assignments

5 videosTotal 85 minutes

Machine Learning16 minutes
Models7 minutes
Operationalize Data20 minutes
Data Normalization18 minutes
Decision Tree23 minutes

3 readingsTotal 80 minutes

Reading References30 minutes
Reading References20 minutes
Self-Reflective Reading: Enhancing Machine Learning Skills30 minutes

3 assignmentsTotal 90 minutes

Introduction to Machine Learning Fundamentals12 minutes
Building and Evaluating Machine Learning Classifiers18 minutes
Machine Learning 60 minutes

In this module, you will explore the foundational aspects of Natural Language Processing (NLP) in the context of social media. You will also learn essential techniques such as text pre-processing using NLTK, understanding Part of Speech (PoS) tagging and parsing challenges, and leveraging advanced models like BERT. Along with this, you will gain insights into the history of NLP and tackle specific challenges associated with parsing social media text, preparing you to analyze and interpret digital content effectively.

What's included

5 videos3 readings3 assignments1 ungraded lab

5 videosTotal 50 minutes

History of NLP11 minutes
Text Pre-processing9 minutes
Text Pre-Processing with NLTK10 minutes
PoS and Parsing10 minutes
BERT10 minutes

3 readingsTotal 60 minutes

Reading References10 minutes
Paper Review: CheckThat202020 minutes
Self-Reflective Reading: Exploring the Limitations of NLP Research in Social Media30 minutes

3 assignmentsTotal 90 minutes

Text Processing and NLP Fundamentals12 minutes
Advanced NLP Techniques for Social Media Analysis18 minutes
Natural Language Processing60 minutes

1 ungraded labTotal 60 minutes

Practice Lab: Exploring NLP Techniques in Social Media Reviews60 minutes

In this module, you will delve into the intricacies of sentiment analysis, exploring its various types such as Sentiment 140 and Aspect-Based Sentiment Analysis. You will understand the methodologies and tools used to perform sentiment analysis on social media content. You will also get a chance to address the challenges inherent in sentiment analysis and discuss emerging research trends aimed at enhancing accuracy and applicability in diverse contexts.

What's included

4 videos2 readings3 assignments1 ungraded lab

4 videosTotal 32 minutes

Sentiment Analysis Part 19 minutes
Sentiment Analysis Part 214 minutes
Sentiment 1404 minutes
Aspect Based Sentiment Analysis4 minutes

2 readingsTotal 60 minutes

Reading References30 minutes
Self-Reflective Reading: How Chatbots and Large Language Models Work?30 minutes

3 assignmentsTotal 90 minutes

Types and Fundamentals of Sentiment Analysis15 minutes
Challenges and Innovations in Sentiment Analysis15 minutes
Sentiment Analysis60 minutes

1 ungraded labTotal 60 minutes

Practice Lab: Sentiment Analysis using NLTK on Social Media & Product Reviews60 minutes

In this module, you will dive deep into Topic Modeling, focusing on Latent Dirichlet Allocation (LDA) and its variants. You will learn to apply these techniques to analyze and extract topics from social media content. You will also explore how to construct semantic networks tailored for social media applications, enhancing your ability to uncover hidden thematic structures and insights within textual data.