This example uses the dictionary model file default_sentiment_lexicon.txt.
Input
- Input table: sentiment_extract_input, as in SentimentExtractor Example 1: ModelFile ('dictionary'), AnalysisType ('document')
SQL Call
SELECT * FROM SentimentExtractor ( ON sentiment_extract_input USING TextColumn ('review') ModelFile ('dictionary') AnalysisType ('sentence') Accumulate ('id', 'product') ) AS dt ORDER BY id;
Output
id | product | out_content | out_polarity | out_strength | out_sentiment_words |
---|---|---|---|---|---|
1 | camera | we primarily bought this camera for high image quality and excellent video capability without paying the price for a dslr. it has excelled in what we expected of it, and consequently represented excellent value for me. all my friends want my camera for their vacations. | POS | 2 | excellent 1, capability 1, excelled 1, excellent 1. In total, positive score:4 negative score:0 |
1 | camera | i would recommend this camera to anybody. definitely worth the price. plus, when you buy some accessories, it becomes even more powerful. | POS | 2 | recommend 1, worth 1, powerful 1. In total, positive score:3 negative score:0 |
2 | office suite | it is the best office suite i have used to date. | POS | 2 | best 1. In total, positive score:1 negative score:0 |
2 | office suite | it is launched before office 2010 and it is ages ahead of it already. | NEU | 0 | |
2 | office suite | the fact that i could comfortable import xls, doc, ppt and modify them, and then export them back to the doc, xls, ppt is terrific. | POS | 2 | comfortable 1, terrific 1. In total, positive score:2 negative score:0 |
... | ... | ... | ... | ... | ... |