Skip to content

model for abuse detection and a chrome extension for automatically hiding the trolls or hateful messages mainly built for twitter users.

Notifications You must be signed in to change notification settings

anshika208/Abuse-Detection

Repository files navigation

Tokenizer for Hindi

This package tends to implement a Tokenizer and a stemmer for Hindi language.

To import the package,

from HindiTokenizer import Tokenizer

This package implements various funcions, which are listed as below:

The Tokenizer can be created in two ways

t=Tokenizer("यह वाक्य हिन्दी में है।")

Or

t=Tokenizer()
t.read_from_file('filename_here')

A brief description about all the functions

read_from_file

This function takes the name of the file which is present in the current directory and reads it.

t.read_from_file('hindi_file.txt')

generate_sentences

Given a text, this will generate a list of sentences.

t.generate_sentences()

print_sentences

This will print the sentences generated by print_sentences.

t.generate_sentences()
t.print_sentences()

tokenize

This will generate a list of tokens from the given text

t.tokenize()

print_tokens

This will print the sentences generated by print_tokens.

t.tokenize()
t.print_tokens()

generate_freq_dict

This will generate a dictionary of frequency of words and return it.

freq_dict=t.generate_freq_dict()

print_freq_dict

This will print the dictionary of frequency of words generated by generate_freq_dict.

freq_dict=t.generate_freq_dict()
t.print_freq_dict(freq_dict)

generate_stem_word

Given a word, this will generate its stem word.

word=t.generate_stem_word("भारतीय")
print word
भारत

generate_stem_dict

This will return the dictionary of stemmed words.

stem_dict=t.generate_stem_dict()

print_stem_dict

This will print the dictionary of stemmed words generated by generate_stem_dict.

stem_dict=t.generate_stem_dict()
t.print_stem_dict(stem_dict)

remove_stopwords

This will remove all the stopwords occuring from the given text.

t.remove_stopwords()

clean_text

This will remove all the punctuation symbols occuring in the given text.

t.clean_text()

len_text

Given a text, this will return the length of it.

print t.len_text()

sentence_count

Given a text, this will return the number of sentences in it.

print t.sentence_count()

tokens_count

Given a text, this will return the number of tokens in it.

print t.tokens_count()

concordance

Given a text, and a word, it will print all the sentences where that word is occuring.

sentences=t.concordace("हिन्दी")
t.print_sentences(sentences)

About

model for abuse detection and a chrome extension for automatically hiding the trolls or hateful messages mainly built for twitter users.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages