![]() ![]() ![]() There are so many contractions in the text we type so to expand them we will use the contractions library. The reference of code is taken from here Removing Contractions Let’s see if we remove the emotions and put alternative words, for example, removing this “:-)” emoticon and replacing it with text such as Happy face smiley or any custom name you like. ![]() ![]() While sometimes we don’t want the emoticons so, we remove them but what if I say there is a way around it. The code of Removal of emoticons is taken from: here #I had such high hopes for this dress 15 size really wanted it to work for me The EMOTICIONS dictionary consists of the symbols and names of the emoticons you can customize the EMOTICONS as per your need. The below helper function help to remove the emoticons from the text. While doing the text analysis of Twitter and Instagram data we often find this emoticon and nowadays, there is hardly any text which doesn’t contain any emoticons in them. The code of Removal of emojis is taken from: here Removing Emoticons Well, when we are performing text analysis in some cases removal of emojis is the correct way as sometimes they don’t hold any information.īelow is the helper function from which the emojis will be replaced with the blank. Growing users of the audience on the social media platforms, well there is a significant explosion of usage of emojis in day-to-day life. 'I had such high hopes for this dress' Removing Emojis What if the text has more than just one punctuation in them let’s look at the below example to understand it. Importing the library import pandas as pd Replacing the repetitions of punctations.We will see how to code and clean the textual data for the following methods. Most common methods for Cleaning the Data But if we look in general and just want an overview then follow the article for it. Well, honestly there are many more things that a trained eye can see. Some of the words are combined with the hyphen or data having contractions words.Data is having a mixture of more than one language Some of the text parts are not in the English language.(If the text is from Twitter or Facebook) Text is full of emojis and emoticons and username and links too.Having too many numbers and punctuations (E.g.Having too many typos or spelling mistakes in the text.Take a couple of minutes and explore the data. Text cleaning is task-specific and one needs to have a strong idea about what they want their end result to be and even review the data to see what exactly they can achieve. In this article, we will see some common methods and their code to clean the textual data. Here, the steps of processing the textual data depend on the use cases.įor example, in sentiment analysis, we don’t need to remove emojis or emoticons from the text as they convey the sentiment of the text. Well, there are various types of text processing techniques that we can apply to the text data, but we need to be careful while applying and choosing the processing steps. Well, cleaning of data depends on the type of data and if the data is textual then it is more vital to clean the data. Cleaning is important for model building. I n any machine learning task or data analysis task the first and foremost step is to clean and process the data. This has to be done manually where it exists.This article was published as a part of the Data Science Blogathon. Text Cleaner does not remove highlighting/background colour from list bullets or numbers. This is why Text Cleaner will not allow you to select both ‘preserve underling’ and ‘remove links’.Ħ. This is an idiosyncrasy of Google App Scripts. Removing links also removes underlining in any selected text, even if that text is not a link. It is recommended that the user disables these when they are not needed to make the add-on run more efficiently.ĥ. The options to remove various annoyances are based on complicated scripting. Line breaks in copied text are often actually paragraph breaks, so be sure to select this option if line breaks remain after cleaning.Ĥ. This splits words in half if they contain a line break mid-word. Text Cleaner replaces line breaks with spaces. Paragraph-level attributes such as line spacing and indent are only cleared when entire paragraphs are selected.Ģ. This leaves headings and paragraphs that conform to the document’s styles.ġ. It also removes line breaks, multiple spaces and other annoying features often present in text copied from elsewhere. Text Cleaner is essentially a much more sophisticated version of the ‘clear formatting’ command which allows the user to preserve things like italics and bold. ![]()
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