How to solve ambiguity in sentiment analysis?

ikayunida123ikayunida123 Member Posts: 17 Contributor II
edited August 2020 in Help

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

  • JEdwardJEdward RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 578 Unicorn
    Solution Accepted

    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.


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