sentimentr
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trinker
Dictionary based sentiment analysis that considers valence shifters
Hi, I would like to know if there is a feature implementation for `sentimentr` for German-language text as well?. Particularly the `highlight()` method over the corpus data.
extract_sentiment_terms() currently labels the word "black" as 'negative' and labels "white" as 'positive' sentiment. I realize that in English and most languages, we have used "black" in the negative context...
A parallel option that runs `sentiment` and `sentiment_by` on multiple cores
Mailed Case and Changes description to [email protected]
- http://www.cs.utah.edu/~huangrh/official-sarcasm-cameraReady-v2.pdf - http://stackoverflow.com/questions/14097388/can-an-algorithm-detect-sarcasm Difficult task for 100% accuracy but there may be key features that are highly correlated with a sarcastic comment that would improve sentiment detection. The idea...
Like questiins...quotes are often not indicative of the speakers sentiment. Consider a way to weight these similar to questions.
https://medium.com/@datancoffee/opinion-analysis-of-text-using-plutchik-5119a80229ea http://examples.yourdictionary.com/examples-of-interjections.html
This would belong in textclean but things that are abbreviated forms like fan vs fanatic: ``` > sentiment(c("He's a nice guy", "can be a jerk. I'm not a fan.")) element_id...
I am running some polarity computation through the function `sentiment()`. What I am experiencing is, even for small piece of text, a huge amount of allocated RAM. Sometimes I get...
