This example uses a dictionary table, sentiment_word, instead of a model file.
Input
- Input table: sentiment_extract_input, as in SentimentExtractor Example 1: ModelFile ('dictionary'), AnalysisType ('document')
- dict: sentiment_word
word | opinion |
---|---|
screwed | 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 |
crap | -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, small 0, love 1. In total, positive score:2 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 | screwed 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 | crap -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 |