Text cleaner python
An apostrophe is not considered as punctuation here. word_tokenize: It is a generic tokenizer that separates words and punctuations. There are mainly 3 types of tokenizers.Ī. Tokenization: Splitting a sentence into words and creating a list, ie each sentence is a list of words. One can easily convert the string to either lower or upper by using:įor example, you can convert the character to either lower case or upper case at the time of checking for the punctuations.Ĥ. As python is a case sensitive language so it will treat NLP and nlp differently. In this, we simply convert the case of all characters in the text to either upper or lower case. Text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" Punctuations can also be removed by using a package from the string library. Punctuations can be removed by using regular expressions.ĬODE: text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" The punctuation, when attached to any word, will create a problem in differentiating with other words. The punctuations present in the text do not add value to the data. So to start with we will remove these extra spaces from each sentence by using regular expressions. Most of the time the text data that you have may contain extra spaces in between the words, after or before a sentence. Let’s get started with the cleaning techniques! Cleaning (or pre-processing) the data typically consists of a number of steps. Cleaning up the text data is necessary to highlight the attributes that you’re going to want your machine learning system to pick up on. This data needs to be cleaned before analyzing it or fitting a model to it. The data scraped from the website is mostly in the raw text form. To get an understanding of the basic text cleaning processes I’m using the NLTK library which is great for learning. Spacy works well with large information and for advanced NLP. There are other libraries as well like spaCy, CoreNLP, PyNLPI, Polyglot. One can compare among different variants of outputs. This library provides a lot of algorithms that helps majorly in the learning purpose. The output is in the form of either a string or lists of strings. NLTK is a string processing library that takes strings as input. This article was published as a part of the Data Science Blogathon.