Sentiment Analysis for Market Research and Social Media Training Course

Sentiment Analysis for Market Research and Social Media Training Course

Introduction

Understanding consumer sentiment has become an essential pillar in the digital marketing landscape. With the rise of social media and online reviews, organizations must harness sentiment analysis to decode customer emotions, monitor brand perception, and make data-driven decisions. Sentiment Analysis for Market Research and Social Media Training Course empowers learners with the latest tools and techniques in natural language processing (NLP), machine learning, and social listening, equipping them to extract meaningful insights from vast unstructured data.

Designed for professionals, analysts, and strategists, this industry-relevant training covers real-time sentiment tracking, text classification, and AI-powered opinion mining for enhanced market research, competitive intelligence, and social media analytics. Learners will work with powerful platforms like Python (NLTK, TextBlob, VADER), R, RapidMiner, and Power BI while applying concepts through practical case studies across multiple sectors.

Course Objectives

1. Define sentiment analysis and its relevance in digital marketing.
2. Identify sentiment types using NLP techniques.
3. Apply machine learning algorithms to sentiment classification.
4. Use Python libraries for text preprocessing and analysis.
5. Interpret customer feedback from social media platforms.
6. Integrate sentiment analysis into market research strategies.
7. Visualize sentiment data using Power BI and Tableau.
8. Automate opinion mining using AI tools.
9. Analyze brand perception over time using trend tracking.
10. Perform sentiment analysis on multilingual data.
11. Design dashboards for real-time sentiment reporting.
12. Evaluate the accuracy and limitations of sentiment models.
13. Implement actionable insights from sentiment data into business strategy.

Target Audiences

1. Digital marketers
2. Market researchers
3. Social media managers
4. Data analysts
5. Brand strategists
6. Business intelligence professionals
7. Customer experience teams
8. Tech-savvy entrepreneurs

Course Duration: 5 days

Course Modules

Module 1: Introduction to Sentiment Analysis

• Importance of sentiment analysis in market research
• Types of sentiment: Positive, Negative, Neutral
• Overview of tools and platforms
• Applications in business and social media
• Challenges and limitations
• Case Study: Analyzing Twitter sentiment on a product launch

Module 2: Text Preprocessing and Cleaning Techniques

• Tokenization, stemming, and lemmatization
• Stopword removal and normalization
• Handling emojis, slang, and misspellings
• Text vectorization (TF-IDF, Bag of Words)
• Building a clean corpus for analysis
• Case Study: Cleaning Yelp reviews for customer sentiment

Module 3: Sentiment Classification with Python

• Introduction to TextBlob, VADER, and NLTK
• Rule-based vs. machine learning approaches
• Naive Bayes and SVM classifiers
• Evaluating classification performance
• Sentiment scoring and interpretation
• Case Study: Python-based analysis of Amazon product reviews

Module 4: Social Media Sentiment Analysis

• Social media APIs (Twitter, Facebook, Instagram)
• Extracting and storing social media data
• Hashtag and trend analysis
• Real-time monitoring and alert systems
• Influencer impact measurement
• Case Study: Brand sentiment tracking during a PR crisis

Module 5: Visualizing Sentiment Data

• Introduction to Power BI and Tableau
• Data storytelling techniques
• Building sentiment dashboards
• Heatmaps, word clouds, and trend lines
• Real-time sentiment report automation
• Case Study: Visual dashboard of customer feedback for a telecom firm

Module 6: Sentiment Analysis for Market Research

• Integrating customer voice into product development
• Competitive sentiment benchmarking
• Consumer behavior and preference analysis
• Linking sentiment to business KPIs
• Campaign performance evaluation
• Case Study: Competitor sentiment comparison in the fashion industry

Module 7: Advanced Techniques in Sentiment Mining

• Deep learning models (LSTM, BERT for sentiment)
• Multilingual sentiment analysis
• Sarcasm and irony detection
• Aspect-based sentiment analysis
• Ensemble methods for improved accuracy
• Case Study: Aspect-based sentiment analysis for hotel reviews

Module 8: Strategy and Business Integration

• Embedding sentiment insights into business workflows
• Customer journey optimization using sentiment data
• Enhancing customer experience through proactive responses
• ROI measurement of sentiment campaigns
• Ethical considerations and data privacy
• Case Study: Using sentiment trends to guide rebranding strategy

Training Methodology

• Interactive instructor-led live sessions
• Hands-on lab exercises using real-world datasets
• Python and Power BI coding demonstrations
• Case study analysis and group presentations
• Final capstone project and peer feedback
• Continuous access to course materials and community forum

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.