Text reflects a number of emotions of the originator. Learn to extract emotions from text using the sentimentr package.
- [Instructor] The sentimentr package … also provides functions to analyze the emotions … of the reviews. … We will use it to understand emotions … in the review text. … First, we use the emotions_by function … to identify emotions for each review … and convert that into a DataFrame. … Let's run the code … and see the DataFrame created. … For each review, and for each emotion, … the results provide a score … and a count. … The count indicates the number of times a word representing … that emotion is present in the review. … The more the count, the stronger the emotion. … For example, in review number one, … the emotion type anger is present once. … In review number three, … a word representing disgust is present twice. … We will now aggregate the DataFrame … to summarize the emotions. … We aggregate count by emotion type. … Then we remove all records in the summary … where the count is zero. … Let's execute the code … and view the results. … This provides the overall emotions in the review corpus. …
- 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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