Comments (4)
I do not understand. You said
I initially cleaned the text (lower case, changed numbers into letters etc) and used qdap's polarity function, this found different avg polarity in the three groups (-0.5, -0.3, -0.2). I then found the sentimentr package and ran my data through that and got almost identical answers (-0.22,-0.25, -0.21).
This indicates the two algoriithms are similar?
Additionally, I would not expect sentimentr and qdap to be identical. sentimentr is an improvement on both speed and accuracy of qdap. IMO they are not comparable.
Also, a second question-should I clean + lemmatise the responses in both packages?
You might want to use some of the replace_
functions for cleaning. That's a researcher call but sentimentr makes these tools available. As far as lemmatise, this is not a requirement of either package but as a researcher you may have a reason to do so but in my experience I have never lemitized before using sentimentr.
from sentimentr.
Sorry, I don't mean that (-0.5, -0.3, -0.2) and (-0.22,-0.25, -0.21) are the same, but that (-0.22,-0.25, -0.21) are very similar while (-0.5, -0.3, -0.2) are not. The algorithms are not similar, but I'm trying to decide which to use, it sounds like sentimentr is the better package to use?
Thank you so much for your answers!
from sentimentr.
from sentimentr.
thanks!
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Related Issues (20)
- Any hint why the two emoji approaches are different and in what circumstance which one is better?! HOT 1
- Amplifiers
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- Sentimentr split words when there is an accented character HOT 5
- Changing polarity of words in the dictionary HOT 2
- Question about 'highlight' HOT 1
- how to calculate sum of sentiment? HOT 1
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- .mygsub is slow HOT 8
- Transparency for lexicon::hash_sentiment_jockers_rinker HOT 1
- What is the theoretical range for the "sentiment" variable? HOT 1
- I wonder why this sentence is recognized as positive HOT 2
- Polarity categorization? HOT 1
- relabel "black" and "white" as neutral sentiments (black is currently negative; white is labeled positive)
- Overall positive and negative scores given a piece of text
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- how to get pos, neg, neu other than ave_sentiment ?
- plot.sentiment_by ordered fix?
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