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Twitter Sentiment Analysis- results bias positive/negative

mbinsaipmbinsaip Member Posts: 5 Contributor II
edited November 2018 in Help

Hi,

I am new user of RapidMiner. I am following some tutorial from rapidminer users in youtube to build a twitter sentiment analysis process. It was run well but the result was not really good. For example, when I used decision tree, it will give all positive sentiment (no negative or neutral). When I used SVM, the results will all negative. My trainning data was  mixed positive and negative. Can anaybody advice. Thanks.

 

Regards,

saip 

Answers

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,254   Unicorn

    It's very difficult to say without knowing the details of the data and looking at the model output.  It is possible that your data does not contain sufficient attributes with predictive power to generate a differentiated model.  So, for example, your decision tree may not have any splits at all and just contains the root class, which simply predicts the majority class.

     

     

     

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761   Unicorn

    So this highlights the issue that some algorithms are better than others for a particular task.  If this is a standard binonmal (2 labels) classification task, you could try using the ROC operator and embed different algorithms in to do a "model bakeoff" and see which one gives you the best general AUC. 

    Telcontar120
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