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Sentiment Analysis | Interpret the output of Naive Bayes Classification
Learner I
Hello Rapidminer Community,
Many thanks in advance to anyone who helps
I´m currently analysing Employer Reviews from four different companies on the Employer Review Platform "Kununu" for my Bachelor Thesis.
What I´m trying to do is finding different topics/words that reoccur in either positive or negative reviews. So for example if "Salary" "pay" "paid" occurs more often in negative reviews it could mean that a company has to update their salary structure to improve Employee Satisfaction.
I thought of using Naive Bayes to do that. What I basically did is, I labelled the reviews as either positive or negative, then I applied the process I attached to this comment.
The Output of this process features a list of words and there likelyhoods to occur in either positive or negative texts (from my understanding of what NB does). For example I attached the likelyhoods of the word "Berufseinstieg" (which translates to "starting to work").
In the next step I would group similar words like (sticking to the example) "Salary" "pay" "Money" and interpret the likelyhoods to find factors that Employees tend to find great or bad.
Could I use the output of NB in the way I described or would that be wrong because I am quite new to sentiment analysis and I could not find any papers with studies that did something like this.
Thanks a lot in Advance and have a great day
Many thanks in advance to anyone who helps
I´m currently analysing Employer Reviews from four different companies on the Employer Review Platform "Kununu" for my Bachelor Thesis.
What I´m trying to do is finding different topics/words that reoccur in either positive or negative reviews. So for example if "Salary" "pay" "paid" occurs more often in negative reviews it could mean that a company has to update their salary structure to improve Employee Satisfaction.
I thought of using Naive Bayes to do that. What I basically did is, I labelled the reviews as either positive or negative, then I applied the process I attached to this comment.
The Output of this process features a list of words and there likelyhoods to occur in either positive or negative texts (from my understanding of what NB does). For example I attached the likelyhoods of the word "Berufseinstieg" (which translates to "starting to work").
In the next step I would group similar words like (sticking to the example) "Salary" "pay" "Money" and interpret the likelyhoods to find factors that Employees tend to find great or bad.
Could I use the output of NB in the way I described or would that be wrong because I am quite new to sentiment analysis and I could not find any papers with studies that did something like this.
Thanks a lot in Advance and have a great day
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