It is more accurate as it uses more informed analysis to create groups of words with similar meanings based on the context, so it is complex and takes more time. It helps to bring words to their dictionary form. Lemmatization: Takes the word to its root form called Lemma. OR ss = nltk.SnowballStemmer(language = 'english')ī. There are two main types of stemmer- Porter Stemmer and Snow Ball Stemmer(advanced version of Porter Stemmer). The stemmed word might not be part of the dictionary, i.e it will not necessarily give meaning. Stemming: A technique that takes the word to its root form. Print(words_new) Can give condition to get words with length greater than 2 after removing stopwords 6. These are removed after tokenizing the text.ĬODE: stopwords = ('english') So these words must be removed which helps to reduce the features from our data. Stopwords include: I, he, she, and, but, was were, being, have, etc, which do not add meaning to the data. In this, you can use the normal regular expression functions to separate the words.Ī = 'What are your views related to US elections ', a) regexp_tokenize: It can be used when we want to separate words of our interests which follows a common pattern like extracting all hashtags from tweets, addresses from tweets, or hyperlinks from the text. Tweet.tokenize(text) Observe the highlighted part here and in word tokenizeĬ. TweetTokenizer: This is specifically used while dealing with text data from social media consisting of emoticons. Notice that the highlighted words are split based on the punctuations.ī. 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. For example, some techniques and tools will be very precise when counting the individual words, and they may count a lower-case and an upper-case version of the same word separately.This article was published as a part of the Data Science Blogathon.

You will need to know a bit about your analysis methods and the tools you'll be using before you know what type of cleaning you need to do. Most cleaning and parsing for text analysis involves increasing the regularity (for example, fixing typos) or adding structure (tagging certain words as important, or even splitting documents up into different sections that have special meaning - title, authors, chapters, etc.). Most text is created and stored so that humans can understand it, and it is not always easy for a computer to process that text. Before you begin a text analysis project, you often need to clean and parse the text to ensure it is in a format that a computer can use (machine readable).Ĭomputers work well when there is structure to a data source or, at least, some regular patterns that it can identify.

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