This example uses a dictionary table, sentiment_word, instead of a model file.
Input
- Input table: sentiment_extract_input, as in SentimentExtractor Example: InputModelFile ('dictionary'), AnalysisType ('document')
- Dict: sentiment_word
| word | opinion |
|---|---|
| messed | 2 |
| excellent | 2 |
| incredible | 2 |
| terrific | 2 |
| outstanding | 2 |
| fun | 1 |
| love | 1 |
| nice | 1 |
| big | 0 |
| update | 0 |
| constant | 0 |
| small | 0 |
| mistake | -1 |
| difficulty | -1 |
| disappointed | -1 |
| not tolerate | -1 |
| stuck | -1 |
| terrible | -2 |
| junk | -2 |
SQL Call
SELECT * FROM SentimentExtractor (
ON sentiment_extract_input PARTITION BY ANY
ON sentiment_word AS Dict DIMENSION
USING
TextColumn ('review')
AnalysisType ('document')
Accumulate ('id', 'product')
) AS dt ORDER BY id;
Output
id product out_polarity out_strength out_sentiment_words -- ------------ ------------ ------------ ------------------------------------------------------------------------- 1 camera POS 2 excellent 2, excellent 2. In total, positive score:4 negative score:0 2 office suite POS 2 terrific 2, terrific 2. In total, positive score:4 negative score:0 3 camera POS 2 nice 1. In total, positive score:1 negative score:0 4 gps POS 2 outstanding 2, incredible 2. In total, positive score:4 negative score:0 5 gps POS 2 nice 1, big 0. In total, positive score:1 negative score:0 6 gps NEU 0 update 0. In total, positive score:0 negative score:0 7 gps POS 2 messed 2. In total, positive score:2 negative score:0 8 camera NEG 2 stuck -1, difficulty -1. In total, positive score:0 negative score:-2 9 television NEG 2 junk -2, big 0. In total, positive score:0 negative score:-2 10 camera NEG 2 not tolerate -1, constant 0. In total, positive score:0 negative score:-1
Download a zip file of all examples and a SQL script file that creates their input tables.