Discovering meaning in big data and finding important regularities through fundamental techniques and areas where text analytics is deployed.

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We cover how raw textual data is pre-processed, the natural language techniques (NLP) are used to prepare data for subsequent analysis and the paradigms used for system evaluation. The key techniques used in text analytics will be reviewed; including techniques for computing similarity, classification and clustering of texts, sentiment analysis, and discovering temporal regularities. Classic examples of text analytics from social media, polling, predictive analytics and news media will be discussed as examples of the application of these techniques.

1. Text Processing
2. Simple Frequencies
3. Beyond Frequencies
4. Similarity
5. Classification
6. Clustering
7. Evaluation
8. Sentiment Identification
9. Sentiment Use