AI in Sentiment Analysis Course

The Oxford Training Centre’s course on AI in Sentiment Analysis deepens the insight into the main techniques of sentiment analysis driven by AI. In this week-long training program, participants will get the necessary skill to understand and apply sentiment analysis using state-of-the-art AI tools. With the strong emphasis on NLP, machine learning, and deep learning, this course will grant the learner substantial powers to analyze emotional tones in text data originating from various sources such as social media, customer feedback, and marketing campaigns. The course covers a wide range of practical applications in AI-driven Social Media Analytics and Customer Sentiment Analysis with AI, thus enabling participants to harness Machine Learning in Text Analysis for better business insight and strategy.

Objectives and target group

Objectives

The main objective of the Sentiment Analysis Course is to equip learners with substantial theory and practical involvement in sentiment analysis using AI techniques. Upon completion of this course, learners will:

• Understand in detail the Sentiment Analysis using Machine Learning with practical implementation in a real-world setting.
• Have practical experience with Text Mining in Sentiment Analysis, using tools such as Python and NLP in extracting insight from text.
• Deep Learning for Sentiment Analysis: Learn how to develop models capable of predicting customers’ sentiments with high accuracy.
• Understand the role and applications of Emotional AI in analyzing and interpreting customer emotions, particularly in Marketing and Branding.
• Learn various techniques of Sentiment Analysis for Social Media Monitoring that would help in tracking and analysis of public opinions on different media platforms.
• Explore the crossroads where AI and sentiment analysis meet in this AI Sentiment Analysis Training to train the students for the advanced applications in business, technology, and research.

Target Group

The course is highly beneficial for:

• Data analysts and data scientists aiming to enhance their expertise in Machine Learning for Text Analysis.
• Marketing professionals who wish to understand Customer Sentiment Analysis with AI to improve customer engagement strategies.
• Business professionals involved in branding and marketing who need insights into Sentiment Analysis in Marketing and Branding.
• Technology enthusiasts eager to dive deeper into Deep Learning for Sentiment Analysis and its potential applications across various industries.

Course Content

The course material is designed to provide a comprehensive learning experience, covering both theoretical knowledge and practical applications of AI in Sentiment Analysis, including the following:

1. Introduction to Sentiment Analysis and Artificial Intelligence

• Overview of Sentiment Analysis: Understanding its importance in various sectors such as marketing, customer service, and social media monitoring.
• History and Evolution: A brief history of Sentiment Analysis, the role of Artificial Intelligence in Sentiment Analysis, and the development of text mining techniques.
• Key Concepts in NLP: Introduction to important Natural Language Processing (NLP) concepts and how they relate to sentiment analysis.

2. Sentiment Analysis Techniques and Tools

• Types of Sentiment: Understanding the different types of sentiment—positive, negative, and neutral.
• Machine Learning for Text Analysis: Detailed insights into supervised and unsupervised learning techniques used in sentiment analysis.
• Hands-on Python Training: Practical training on using Python for sentiment analysis, including the use of libraries like NLTK, SpaCy, and TensorFlow.

3. Deep Learning for Sentiment Analysis

• Deep Learning Models: Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks used in sentiment analysis.
• Building and Training Models: Techniques for building and training deep learning models to predict sentiment.
• Case Studies: Examining case studies and examples of AI-driven Social Media Analytics using deep learning.

4. Applications of Sentiment Analysis in Business

• Customer Sentiment Analysis with AI: Learning how to implement sentiment analysis to gain insights from customer reviews, feedback, and surveys.
• Sentiment Analysis in Marketing and Branding: Exploring real-world applications, with case studies of companies using sentiment analysis for brand monitoring.
• Social Media Monitoring: Understanding how to leverage sentiment analysis for social media monitoring, tracking public sentiment, and making informed decisions.
• Future of Emotional AI Applications: Discussing the potential future of Emotional AI and how it will transform customer relationships and business strategies.