Text Classification – Natural Language Processing With Python and NLTK p.11
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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we’re ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.
To start, we’re going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we’ll be doing, positive sentiment or negative sentiment.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
Source
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2020 batch Present sir
what kind of variable types do I need in my for loop if I want to make my own categories and own file of word
the double for loop is vague to me because I have no idea what fileid actually does
That is kind of English Language.. Like Literally .. lol
Make a video with the word2vec library please!
can anyone explain why the neg category output wasn't printed ? Both the category words should've been printed as movie_reviews.categories has 2 categories pos and neg.
Sir where to kept that file the document file
sir may I ask what is the fileid for?
Suppose if I have a directory of files and images and videos, using NLP i should train the model such that by passing a text message I have to access that file directly
i have been programming for a few years now but i don't know how do i become an expert in the language. pls help
Can you please tell us where do you learn all this stuff? It will be really helpful
where is the positive and negative text files
Great tutorials!
I am trying to create a classifier similar to this one but using a labeled pandas data frame with WhatsApp messages, in place of the movie_reviews corpus. I am stuck on the step of creating this list you call documents (very important!). All I want to know is if this type of list would be possible from a dataset of labeled messages?
Error := "FreqDist" pbject is not callable, please help
so, please explain whaat is FreDist() working ?
Can anyone explain how creating of list of common words, irrespective of classes, in the entire corpus would help as a feature?
Can someone pls tell me. I am getting an error list object not callable 4:58. And moreover how is append function accepting two parameters?
For those who are watching on 2019: Scikit has text feature extractor ready to use that includes TF-IDF.
can i get a corpus for email classification.. like password reset rquest,customer enquiry mail,feedback mail,complaint mail…. sometime for this?
Append Function has only a single argument?
6:45 It's actually naive because it assumes all variables are independable :> <3 Love you Harrison!
It was just awesome!Literally, Man!Great Work!
This is an Amazing video! Everything well explained!
Great, I think we can also use for multi class classifiaction, not only two class
it has the word 'f*ck' 17 times
I want to verify that a word provided by a user is a particular part of speech. Ex: user prompted to enter a noun; how do I use nltk to verify that word is a noun?
First of all, I want to thank you for your great tutorial.
I have Python 3.6.5 and when I call movie_reviews.words() function, it gives me an "AttributeError: can't set attribute".
Could anybody help me about this? What should I do to resolve this problem?
how to separate and count "positive" and "negative word" in a comment???
Hi I have a question that i would like your expertise on
Firstly i would like to say that your tutorial is great. It aided me a lot in my previous semester work. However i need some specific help.
I am currently doing a project where i am basically collecting questions that are asked and generating various "tags" for each question. These tags allows analyst to understand what the context of the question is.
So far i have thought about using topic modelling algorithm as well as pre processing methods that you have shared in your previous tutorial. What got me confused is how do we get the context of the question.
For example the question "What is networking" is different when asked by someone majoring in Computer Science and someone who is in the business field! Any tips? I would greatly appreciate it hehe
all_words actually has 39768 words 😛