Text = list(cont.expand_texts(, precise=True)) This function should also work for this: from pycontractions import Contractions With the help of this function, this sentence: Pattern = re.compile('()'.format('|'.join(map.keys())), flags=re.IGNORECASE|re.DOTALL)Įxpanded = map.get(match) if map.get(match) else map.get(match.lower()) from contractions import CONTRACTION_MAPĭef expand_contractions(text, map=CONTRACTION_MAP): I will give the reason for this in a later chapter. For the sake of completeness, I list the necessary functions, but do not use them in our following example with the Example String and DataFrame. You can do expanding contractions but you don’t have to. Removes extra whitespaces from a string, if present Return re.sub(r'', ' ', text) def remove_extra_whitespaces_func(text): Removes all irrelevant characters (numbers and punctuation) from a string, if presentĬlean string without irrelevant characters Return re.sub(r'', ' ', text) def remove_irr_char_func(text): Removes all punctuation from a string, if present Return unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore') def remove_punctuation_func(text): Removes all accented characters from a string, if present Return re.sub(r'https?://\S+|www\.\S+', '', text) def remove_accented_chars_func(text): Removes URL addresses from a string, if present Return BeautifulSoup(text, 'html.parser').get_text() def remove_url_func(text): Text (str): String to which the function is to be applied, string Removes HTML-Tags from a string, if present I will show them again in the course of this post at the place where they are used.
df = df.astype(str)Īll functions are summarized here. To be on the safe side, I convert the reviews as strings to be able to work with them correctly. Let’s take a closer look at the first set of reviews: df.iloc
However, we will only work with the following part of the data set: df = df] Nltk.download('maxent_ne_chunker') import pandas as pdįrom import PorterStemmerįrom import WordNetLemmatizerįrom wordcloud import WordCloud df = pd.read_csv('Amazon_Unlocked_Mobile_small.csv') Nltk.download('averaged_perceptron_tagger')
Text cleaner python download#
If you are using the nltk library for the first time, you should import and download the following: import nltk
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