Explore qdap's polarity & built-in lexicon
Sentiment Analysis in R
Ted Kwartler
Data Dude
polarity()
An example subjectivity lexicon:
| Word | Polarity |
|---|---|
| Amazing | Positive |
| Bad | Negative |
| Good | Positive |
| ... | ... |
| Wonderful | Positive |
Context cluster
Example context cluster:
The DataCamp sentiment course is very GOOD for learning.
Context cluster, continued
Example context cluster:
The DataCamp sentiment course is very GOOD for learning.
| Term | Class | Word Count |
|---|---|---|
| Very | Amplifier | 1 |
| Good | Polarized Term/Positive | 1 |
| All other words | Neutral | 7 |
Context cluster glossary
- Polarized Term - words associated with positive/negative
- Neutral Term - no emotional context
- Negator - words that invert polarized meaning e.g. "not good"
- Valence Shifters - words that effect the emotional context
- Amplifiers - words that increase emotional intent
- De-Amplifiers - words that decrease emotional intent
Context cluster scoring
Example context cluster:
The DataCamp sentiment course is very GOOD for learning.
| Term | Class | Word Count | Polarity Value |
|---|---|---|---|
| Very | Amplifier | 1 | 0.8 |
| Good | Polarized Term/Positive | 1 | 1 |
| All other words | Neutral | 7 | 0 |
Polarity calculation
| Class | Word Count | Polarity Value |
|---|---|---|
| Amplifier | 1 | 0.8 |
| Polarized Term | 1 | 1 |
| Neutral | 7 | 0 |
| Sum | 9 | 1.8 |
Example Context Cluster
The DataCamp sentiment course is very GOOD for learning.
- 1 + 0.8 = 1.8
- 1+1+7 = 9
- $$\frac{1.8}{\sqrt{9}}$$
Answer: 0.6
Let's practice!
Sentiment Analysis in R
