Open trinker opened 7 years ago
Hi, this is a read-only mirror of CRAN, please send your comments to the package authors. THanks!
Came here to say the same thing. Just tried the package with very small sentences and I can confirm it is very slow. Sentences with 7 words take around 2 seconds to calculate sentiment scores. I cannot think of applying the sentiment score function to a dataset with 100K rows. Package needs attention I presume.
Hi! Today only I noticed the issues mentioned in read only mirror of CRAN. I have written my package getting inspired by "sentimentr" only. I decided to experiment with Parts of Speech in this package. However, the running time of the package is not so decent. I am working on it to improve it. I am completely new to R and also to writing package. It is taking me time to improve the algorithm. I will try to rewrite the POS tagger as suggested by @trinker.
@SubhasreeBose Per @gaborcsardi 's request (I accidentally posted here in error) could we move this conversation to your repo? Is there a GitHub repo of your own?
@trinker Here is my GitHub repository which I created just now. https://github.com/SubhasreeBose/RSentiment
I am the author of the sentimentr package and have been benchmarking various sentiment packages against each other for accuracy and speed for about a year now. Your package intrigued me when it came out last year b/c you were attempting things beyond a simple lookup like my own sentimentr package but with different approaches. Last year I benchmarked RSentiment and it had decent accuracy and speed (these two things are a tradeoff). However, I am running some tests now and see that RSentiment is too slow to be a viable solution for text of medium-large size. It's even slower than using R to run coreNLP from Stanford which I consider most accurate of non-machine learning approaches. I looked at the code and see you're using parts of speech. Smart..I've thought about this myself. The problem is in your implementation you're running the tagger over every sentence instead of all the text at once as it is intended to be. Could it be a possibility to re-write the POS tagger to operate on all text at once rather than iterating over all elements in the text vector?