Based on your comment, you seem to be looking for positive words with the intent of performing sentiment analysis (generally lists are divided into positive, negative, and neutral). Here are some lists to get started with (a Google search will turn up plenty more):

• Hu and Liu, KDD, 2004 (~6800 words)
• Breen, Twitter Sentiment Analysis Tutorial, 2011
• Davies, Sentiment Analysis Word List
• University of Pittsburgh MPQA (see also paper)
• There are also several APIs you use for sentiment analysis

As a side note, word lists are only marginally helpful in detecting/classifying sentiment. It will capture the low-hanging fruit, but not more complicated or ambiguous phrases such as "watch the movie" (which is a positive movie review, but a negative book review).

As far as extracting pertainyms goes, any good POS-tagger will allow you to identify the predicted direct object of a verb and whatnot, but as you are likely aware, this is fraught with its own complications.

Based on your comment, you seem to be looking for positive words with the intent of performing sentiment analysis (generally lists are divided into positive, negative, and neutral). Here are some lists to get started with (a Google search will turn up plenty more):

• Hu and Liu, KDD, 2004 (~6800 words)
• Breen, Twitter Sentiment Analysis Tutorial, 2011
• Davies, Sentiment Analysis Word List
• University of Pittsburgh MPQA (see also paper)
• There are also several APIs you use for sentiment analysis

As a side note, word lists are only marginally helpful in detecting/classifying sentiment. It will capture the low-hanging fruit, but not more complicated or ambiguous phrases such as "watch the movie" (which is a positive movie review, but a negative book review).

As far as extracting pertainyms goes, any good POS-tagger will allow you to identify the predicted direct object of a verb and whatnot, but as you are likely aware, this is fraught with its own complications.