Overview

Class Central Tips

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.

Syllabus

Course Introduction

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

Machine Learning

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.

Natural Language Processing

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.

Sentiment Analysis

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.

Topic Modeling

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.