
Parts Of Speech Abbrivations:
CC coordinating conjunction
CD cardinal digit
DT determiner
EX existential there (like: “there is” … think of it like “there exists”)
FW foreign word
IN preposition/subordinating conjunction
JJ adjective ‘big’
JJR adjective, comparative ‘bigger’
JJS adjective, superlative ‘biggest’
LS list marker 1)
MD modal could, will
NN noun, singular ‘desk’
NNS noun plural ‘desks’
NNP proper noun, singular ‘Harrison’
NNPS proper noun, plural ‘Americans’
PDT predeterminer ‘all the kids’
POS possessive ending parent‘s
PRP personal pronoun I, he, she
PRP$ possessive pronoun my, his, hers
RB adverb very, silently,
RBR adverb, comparative better
RBS adverb, superlative best
RP particle give up
TO to go ‘to‘ the store.
UH interjection errrrrrrrm
VB verb, base form take
VBD verb, past tense took
VBG verb, gerund/present participle taking
VBN verb, past participle taken
VBP verb, sing. present, non-3d take
VBZ verb, 3rd person sing. present takes
WDT wh-determiner which
WP wh-pronoun who, what
WP$ possessive wh-pronoun whose
WRB wh-abverb where, when
url = "http://translate.google.com/translate_a/t?client=te&format=html&dt=bd&dt=ex&dt=ld&dt=md&dt=qca&dt=rw&dt=rm&dt=ss&dt=t&dt=at&ie=UTF-8&oe=UTF-8&otf=2&ssel=0&tsel=0&kc=1"
In NLP Boot-camp: Hands-on Text mining in Python using TextBlob for Beginners course, you will learn Text Mining, Sentiment Analysis, Tokenization, Noun Phrase Extraction, N-grams, and so many new things. I will start from a very basic level where I will assume that everyone is an absolute beginner, having no knowledge regarding Machine Learning, Artificial Intelligence, and Natural Language Processing. So, I will be explaining everything in a very easy manner. I will start with Tokenization, Parts-of-speech tagging, Noun Phrase Extraction, Sentiment Analysis, Spell Checking, Words Inflection, Lemmatization, Spell Checking, Words, and Noun Phrase Frequency, and N-grams. Then, I will jump to the intermediate level where I will explain how to develop your own Text Classification System and I will explain what is Naive Bayes Classifier, how to create a model, its training and testing. In the advanced/ final level, I will explain Model Accuracy, then I will again explain about Tokenizer, Sentiment Analyzer, Parts-of-speech Tagger, and Noun Phrase Extractor. For all these projects, I will use one of the easiest Python libraries for Natural Language Processing and that is TextBlob. This NLP Boot-camp: Hands-on Text mining in Python using TextBlob for Beginners course is designed in such a way that after this course, learning other advanced Natural Language Processing Libraries like NLTK, Tensor-Flow, and Keras, etc will be no more difficult for you.