Nltk tutorials clean text data3/17/2023 They don’t add any value while modeling thus it is important to remove all of them from the text. Different symbols, punctuation, and accent marks are considered special symbols. Special symbols are characters that are not considered either letters or digits. x = "it'd've better if less food oil is added." import json abbreviations = json.load(open(' PATH')) for key in abbreviations: if key in x: x = x.replace(key,abbreviations) print(x) Remove Special Symbols You can find the JSON file with the most common abbreviation short form and their full version here on my Github profile. It becomes important to replace abbreviations with their full form otherwise our model will not be able to learn proper patterns from the data. These usually occur in social media datasets. AbbreviationsĪn abbreviation is a shortened form of a word, for example: TTL: Talk to you later. resume font is bad.' import unicodedata unicodedata.normalize('NFKD', doc).encode('ascii', 'ignore').decode('utf-8', 'ignore') - resume length is good. These usually occur when you try to collect data from a web source, or a multilingual source. These characters cause problems in analysis by increasing the vocabulary size unnecessarily.įor example, résumé and resume are two different words for our model, whereas both of them produce the same meaning. URLs A generic URL contains a protocol, subdomain, domain name, top level domain, and directory path.Īccented Characters Accent marks are symbols used over letters especially vowels to emphasize the pronunciation of a word. doc = 'you can contact me on my work email for any queries.' import regex as re doc) - you can contact me on my work email for any queries. Usually, an email starts with a personalized name followed by some initials like digits, special symbols, etc., then ends with an email service provider. doc = ' Food is very good and cheap.' import regex as re re.sub('','',doc) - Food is very good and cheap.Įmails Gmail is one of the most famous and commonly used service providers for email services. We can remove these completely from our text data or replace them with their representative word. For example hashtags, HTML tags, mentions, emails, URLs, phone numbers, or some special combination of characters. Unwanted data refers to certain parts of the text that don’t add any value in analysis and model building. doc = 'python programming language ' import regex as re re.sub("\s "," ",doc) - python programming language Removing Unwanted Data It is important to remove these before applying any text processing or cleaning technique to the data. Most of the text data you collect from the web may contain some extra spaces between words, before and after a sentence. text = 'Python PROGRAMMING LanGUage.' text.lower() - python programming language. ![]() Both types create problems in analysis thus it is important to normalize the text into lowercase. It is very common for any text data to have words that follow a certain capitalization like camel case, title case, sentence case, etc., or some mis-capitalized words (eg: pYthOn). It is important to apply each step in the same serial manner as mentioned below, otherwise, you could end up losing lots of useful data. In this blog, we will look at some techniques to perfectly clean text data for natural language processing. One of the most important steps of creating these models is converting raw text data into a much better-cleaned version that contains only useful information. NLP is a branch of AI that uses text data as input and return models that can understand and generate insights from new text data. In short, natural language processing is a way to provide computers with the ability to understand and communicate in human language. Natural language refers to the medium we humans use to communicate with each other, and processing simply means the conversion of data into a readable form.
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