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.
Artificial Intelligence in Social Media Analytics
This course is part of Social Media Analytics Specialization
Recommended experience
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
Details to know
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September 2024
12 assignments
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There are 5 modules in this course
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.
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
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
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
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.
What's included
4 videos2 readings3 assignments1 ungraded lab
Instructor
Offered by
Recommended if you're interested in Machine Learning
Johns Hopkins University
Johns Hopkins University
Johns Hopkins University
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