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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
- # Copyright (C) 2016 Constant, Algolit
- # This program is free software: you can redistribute it and/or modify
- # it under the terms of the GNU General Public License as published by
- # the Free Software Foundation, either version 3 of the License, or
- # (at your option) any later version.
- # This program is distributed in the hope that it will be useful,
- # but WITHOUT ANY WARRANTY; without even the implied warranty of
- # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- # GNU General Public License for more details: <http://www.gnu.org/licenses/>.
- '''
- Input texts are checked against occurences of certain words included in a list of "stopwords" established by NLTK (Natural Language Toolkit). These words are then removed.
- In data mining, text processing and machine learning, these so-called high frequency words are filtered out before or after natural language data is processed.
- Relational words such as 'the', 'is', 'at', 'which', and 'on' are considered redundant because they are too frequent, and meaningless once the word order is removed.
- '''
- # A set is a list with unique words
- stopwords = set()
- # define list of filtered words
- filtered_words = []
- # read stopwords from file & save them in a list
- # read from file
- # txt = source.readlines()
- with open("english.txt", "r") as source:
- # for each line
- for line in source:
- # clean returns
- line = line.strip()
- # add word to set stopwords (cfr difference with list: list.append())
- stopwords.add(line)
- # define your sentence / string
- sentence = 'I was at Synesthésie last night and took a bus to go home.'
- # print sentence
- print("phrase originale:", sentence)
- # convert string to list of words
- words = sentence.split(" ")
- # for each word of list, check if word is in stopwords, if it isn't, add word to filtered wordlist
- for word in words:
- if word not in stopwords:
- filtered_words.append(word)
- # this is the same, but shorter + no need to declare filtered_words as list in the beginning:
- #filtered_words = [word for word in words if word not in stopwords]
- # turn wordlist into string of characters
- new_sentence = " ".join(filtered_words)
- # print new sentence
- print("phrase réécrite:", new_sentence)
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