How to predict your desired class using Naive Bayes in text classification

Question

I am using Multinomial Naive Bayes Classifier from scratch to classify text in python.

I compute a function counter for each class and a probability distribution for functions.

According to my implementation, I get the following results:

Suppose I have the following body:

corpus = [
            {'text': 'what is chat service?', 'category': 'what_is_chat_service'},
            {'text': 'Why should I use your chat service?', 'category': 'why_use_chat_service'}
        ]

According to Naive Bays for this corpus, the previous probabilities for both classes will be 0.5

If I do some preprocessing, including lowercase, stop word deletion and punctuation, I get the following list of tokens:

• text 1: [chat, service]
• text 2: [use, chat, service]

, " -" :

class                     chat     service   P(class|features)

what_is_chat_service      1        1         0.5
why_use_chat_service      1        1         0.5

2 . .

. , :

class                   what      is    chat     service   P(class|features)

what_is_chat_service    1         1     1        1         0.5(higher)
why_use_chat_service    1e-9      1e-9  1        1         5e-19

= 1e-9

i.e,

1: what_is_chat_service

- :

corpus = [
            {'text': 'what is chat service?', 'category': 'what_is_chat_service'},
            {'text': 'what is the benefit of using chat service?', 'category': 'why_use_chat_service'}
        ]

1 .

" -?" .

"what_is_chat_service".

? Naive Bayes sklearn. .

, , .

.

Answer 1

. ( , : , , ).

"-" (, , "-" " " ).

" " " -" , , , . , :

• - ( , )
• - ( , )

( ). , ( ), , , .

, , , . , Naive Bayes, , n- ( ). (, ) .