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.

Prebuilt datasets

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_tagger * punkt * vader_lexicon

Some of the corpora used by sugaroid are * stopwords corpus * wordnet corpus

What is corpus? Corpus is a text file which contains useful information which can be precisely extracted to get useful information. stopwords are words which are commonly used in English speech. Most of the time, stopwords do not contain important meanings of the statement to the bot. stopwords give meaning to robots. Some examples of stopword are if, 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

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.