Social Media Mining with R training in delhi ncr

Social Media Mining with R

Intriduction

• Social media mining using sentiment analysis
• The state of communication
• What is Big Data?
• Human sensors and honest signals
• Quantitative approaches

Getting Started with R(optional)

• Why R?
• Quick start
• The basics – assignment and arithmetic
• Functions, arguments, and help
• Vectors, sequences, and combining vectors
• A quick example – creating data frames and importing files
• Visualization in R
• Style and workflow
• Additional resources

Mining Twitter with R

• Why Twitter data?
• Obtaining Twitter data
• Preliminary analyses

Potentials and Pitfalls of Social Media Data

• Opinion mining made difficult
• Sentiment and its measurement
• The nature of social media data
• Traditional versus nontraditional social data
• Measurement and inferential challenges

Social Media Mining – Fundamentals

• Key concepts of social media mining
• Good data versus bad data
• Understanding sentiments
• Scherer’s typology of emotions
• Sentiment polarity – data and classification
• Supervised social media mining – lexicon-based sentiment
• Supervised social media mining – Naive Bayes classifiers
• Unsupervised social media mining – Item Response Theory for
text scaling

Social Media Mining – Case Studies

• Introductory considerations
• Case study 1 – supervised social media mining – lexicon-based
sentiment
• Case study 2 – Naive Bayes classifier
• Case study 3 – IRT models for unsupervised sentiment scaling

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