"(Sentiment Analysis) How to Assign Weight to Words in Training Set"
Dear Fellow Rapidminer Users,
These days I am working on conducting sentiment analysis on social media data. In my training set I am using the words in order to train the algorithm and every word has a score which shows positivity/negativity. However, they have different level of positivity or negativity. For example:
happy - 4.8 positive
sad - 2.7 negative
brilliant - 4.98 positive (more positive then the word 'happy')
As it can be seen from the example words positivity/negativity level of the words are different. My question is that how can I assign weight to words in traning set instead of labeling them only positive or negative? Which kind of algorithm should I establish in order to conduct sentiment analysis within the indicated framework and do you think that will it be more detaily and efficient when it comes to sentiment analysis?
Answers
Hi,
have a look at Dictionary Based Sentiment Analysis in the operator toolbox extension. That should do the trick.
Cheers,
Martin
Dortmund, Germany
Hey!
Thank you for the answer. Is it possible to state a roadmap? Because, I am not so use to Rapidminer for this kind of complicated process.
Thanks
Hi,
what do you mean by road map? An example how to use it?
If yes, have a look at the help text of the operator. It always provides a tutorial process on how to use it.
Best,
Martin
Dortmund, Germany
Hiii sir!! Did you get the solution for the problem which you posted??If so,please suggest me.I am also searching for the same...