Read the Large Movie Review Dataset [1] (sampled) available at the following path Thedata/MoviereviewDataset_sampled.table. The dataset contains labeled reviews as positive or negative, as well unlabeled reviews. Use the Strings to Document node to transforms the strings into documents and keep only a sample of the documents categorized as positive reviews (first 3 rows). Tag the words available in the documents and pre-process them by filtering the numbers, erase the punctuations, filter the stop words, convert the words in lower case, apply snowball stemmer and use the Tag Filter node to keep only nouns and verbs. Finally, use the Chi-Square Keyword Extractor node to extract 5 keywords per each document and filter the keywords by using 10 as lower bound for the variable Chi value. What are the 5 most valuable keywords? To which document they belong to? [1] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)
This is a companion discussion topic for the original entry at https://kni.me/w/oD9xVSkPG8ZZ9XJR