How to solve ambiguity in sentiment analysis?
Hello everyone :womanhappy:
I'm quite new to the text mining and I'm trying to do the sentiment analysis task today. But I encounter some problems while doing this sentiment analysis :womanfrustrated:
In my language, a word can have some different meanings. Like "setan" can have means : 1) devils 2) cursing words.
How to solve this ambiguity in sentiment analysis? Any tips would be great. Thank you!
Note : The algorthm that I'm using right now is Naive Bayes Classifier.
Best Answer
-
In some cases the algorithm will actually solve the ambiguity for you.
Naive Bayes works by looking at how often a word (or combination of words, if using n_grams) when put together have a certain meaning. If your dataset shows that 60% of the time "setan" means devils & the remaining 40% means cursing then your scoring will mark the meaning as devils.
N grams are where you combine separate words into a single one for example individually "setan" is 60% meaning devil, but "stupid_setan" is 90% of the time cursing.
Naive Bayes is a nice algo that does not need to classify things in a binary way of true & false, but a range of possible choices.
1
Answers
-
In some cases the algorithm will actually solve the ambiguity for you.
Naive Bayes works by looking at how often a word (or combination of words, if using n_grams) when put together have a certain meaning. If your dataset shows that 60% of the time "setan" means devils & the remaining 40% means cursing then your scoring will mark the meaning as devils.
N grams are where you combine separate words into a single one for example individually "setan" is 60% meaning devil, but "stupid_setan" is 90% of the time cursing.
Naive Bayes is a nice algo that does not need to classify things in a binary way of true & false, but a range of possible choices.
1