Predict text for word completion and next word using the n-grams database built with text data.
- [Narrator] In this video, we will use the bigrams database … we generated earlier, to predict text. … The first example we look at is autocompleting a word. … We will predict the word which starts with the letters A-P. … In real world, suppose a user types A-P in this form, … we want to predict what that word would be. … In order to do that, we do a filter on the first word column … to see all the words that starts with A-P. … We are predicting the current word only, … so we only look at the first word and it's frequency. … The same first word can occur in multiple rows … with different second words. … So, we aggregrate across all rows for a given first word. … Finally, we also order them … in descending order of frequency. … Let's execute the code and review the results. … We see that there are three words printed … in descending order of frequency. … This is the list you will present … to the user for autocompletion. … Note that this list is based on the corpus we used. … A different or a bigger corpus can produce …
- Creating a word cloud
- Analyzing sentiment
- Extracting emotions from text
- Clustering similar entities based on text
- Using classification for supervised learning
- Recommending items to users based on text data analytics
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Word Cloud
2. Sentiment Analysis
5. Predictive Text
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