Sugaroid's brains lies in its datasets. It might not make sense and can possibly give wrong replies if its not trained with the default dataset. Its more like "Artificially Foolish" without a dataset.
Sugaroid uses a few well known datasets which helps to increase the accuracy of natural
language processing. These are provided and fetched by nltk and spacy, which are
popular natural language processing libraries used in Python.
A list of datasets include
averaged_perceptron_taggerpunktvader_lexicon
Some of the corpora used by sugaroid are
stopwordscorpuswordnetcorpus
What is corpus? Corpus is a text file which contains useful information which can be precisely extracted to get useful information.
stopwordsare words which are commonly used in English speech. Most of the time,stopwordsdo not contain important meanings of the statement to the bot.stopwordsgive meaning to robots. Some examples of stopword areif,on,is,are, etc.
Wordnet is a collection of arrays of words which have a unique lemma. Some words may be used as an exaggeration, or sometimes, the same word is used in superlative, comparative tones. At many times, its very useful to ignore such words and depend on the lemma (aka root word). Wordnet is a very interesting library that helps to make things simpler.
Vader Lexicon is a zipped sentiment analyzer which contains many statements with vector scores of a respective words. A resultant vector product is take to find out the approximate sentiment polar score (positive or negative statment). However trained, Vader Lexicon is not very accurate its terms, but however, it remains one of the best datasets used in sugaroid!
Punkt is a punctuation library used by Sugar to understand mood of a statement, i.e., interrogative mood, imperative mood, negation, etc.