1. A method, comprising: determining, using a first sentiment model having a current list of keywords and respective weights, respective sentiment results for a plurality of posts, wherein the determining is performed using a processing device; receiving, via a user interface, sentiment feedback for the respective sentiment results; displaying a first set of posts which are in the plurality of posts and have a sentiment feedback not matching the respective sentiment results; displaying: keywords which appear in the first set of posts; and the keywords' respective weights; receiving a first change including at least one of: a first proposed change to a respective weight of one or more of the keywords; a first proposed deletion of one or more of the keywords; or a first proposed new keyword and a respective weight of the first proposed new keyword; maintaining unchanged the current list of keywords and respective weights while creating a proposed list of keywords and respective weights by applying the first change to the current list of keywords and respective weights; analyzing the first set of posts, using the proposed list of keywords and respective weights; enabling identifying overfitting by displaying sentiment results for the first set of posts produced using the proposed list of keywords and respective weights; receiving a second change including at least one of: a second proposed change to a respective weight of one or more of the keywords; a second proposed deletion of one or more of the keywords; or a second proposed new keyword and a respective weight of the second proposed new keyword; updating the proposed list of keywords and respective weights by applying the second change to the proposed list of keywords and respective weights; after applying the second change to the proposed list of keywords and respective weights, re-analyzing the first set of posts using the proposed list of keywords and respective weights; enabling identifying overfitting by displaying sentiment results for the first set of posts produced using the proposed list of keywords and respective weights; and creating a second sentiment model by replacing the current list of keywords and respective weights with the second proposed list of keywords and respective weights.
6. The method of claim 1 , further comprising: calculating, for the sentiment results produced using the proposed list of keywords and respective weights, precision for at least one of negative sentiment, neutral sentiment, and positive sentiment; and displaying the results of the calculating.