Text Preprocessing Pipeline V2

6 min readAug 5, 2023

Greetings! I’m Kishan Tongrao, a Data Scientist, and I’m thrilled to share with you the Text Preprocessing Pipeline v2. Together, we’ll delve into the world of data preparation and uncover the magic behind refining text data. Feel free to connect with me on LinkedIn for any further insights or discussions. Let’s embark on this exciting journey of text preprocessing and unlock the true potential of data!

Index :

What is Text Preprocessing Pipeline?
Components of Text Preprocessing Pipeline
Building/ Coding Text Preprocessing Pipeline

What is Text Preprocessing Pipeline?

In the realm of natural language processing (NLP) and text analytics, a Text Preprocessing Pipeline is a structured sequence of data cleaning and transformation steps applied to raw text data before it can be effectively used for analysis or modeling. The primary objective of this pipeline is to convert unstructured text data into a consistent, clean, and organized format, making it easier for NLP algorithms and models to extract meaningful insights.

By following this systematic pipeline, NLP practitioners can ensure that the text data is refined and processed in a consistent manner, paving the way for more accurate and meaningful results in various NLP applications like sentiment analysis, topic modeling, text classification, and more. Text preprocessing acts as a critical initial step, laying the foundation for successful NLP tasks and enhancing the overall quality of language-based analyses.

Components of Text Preprocessing Pipeline

Below are list of components that I included in the pipeline with proper order which is very important here.

Change to Lowercase → Remove HTML Tags → Remove URLs → Remove Emojis → Remove Emoticons → Convert Emojis → Convert Emoticons → Contraction to Expanded Form → Chat Word Conversion → Spelling Checking and Correcting → Separate Combined Words → Remove Stopwords → Stemming → Lemmatization → Remove Punctuations → Remove Numbers and Extra Spaces

Change to Lowercase

In NLP, lowercase refers to the process of converting all text to lowercase letters. It involves changing all uppercase characters to their corresponding lowercase counterparts while leaving lowercase characters unchanged.

Remove HTML Tags

Removing HTML tags from a text or document is a common task in web development, data processing, and content analysis. HTML tags are used to define the structure and formatting of content on web pages. When you need to extract or clean the actual textual content without any HTML markup, you can perform HTML tag removal using various methods and tools.

Remove URLs

Removing URLs from text or documents is a common preprocessing step in various natural language processing (NLP) and text analysis tasks.
URLs (Uniform Resource Locators) are web addresses that link to specific web pages or online resources. When processing textual data, removing URLs can be beneficial in certain scenarios to improve the quality of analysis and simplify downstream tasks.

Remove Emojis

Removing emojis from text is a common preprocessing step in natural language processing (NLP) and text analysis tasks. Emojis are graphical symbols or icons that represent emotions, objects, or ideas and are widely used in digital communication to convey sentiments and add context to messages. While emojis can enhance the expressive nature of text data, certain NLP applications may require removing them to focus solely on the textual content or to avoid interference with specific analyses.

Remove Emoticons

Removing emoticons from text is a common preprocessing step in natural language processing (NLP) and text analysis tasks. Emoticons are textual representations of facial expressions and emotions, often used in digital communication to convey feelings or sentiments. While emoticons can add emotional context to text data, certain NLP applications may require removing them to focus solely on the textual content or to avoid interference with specific analyses.

Convert Emojis

Converting emojis is a useful technique in natural language processing (NLP) and text analysis tasks to transform graphical emoji symbols into their textual representations. Emojis are graphical icons used to express emotions, objects, or ideas in digital communication. Converting emojis to text allows you to represent emojis in a more interpretable and language-based format.

Convert Emoticons

Converting emoticons is a helpful technique in natural language processing (NLP) and text analysis tasks to transform textual representations of facial expressions and emotions into their corresponding graphical symbols or icons. Emoticons are combinations of keyboard characters used to convey emotions, and they are commonly found in digital communication. Converting emoticons into graphical symbols allows you to represent emotions visually, making the content more expressive and engaging.

Contraction to Expanded Form

Contractions are shortened versions of words or phrases that combine two or more words together. For example, “can’t” is a contraction of “cannot,” and “I’m” is a contraction of “I am.”
https://en.wikipedia.org/wiki/Wikipedia:List_of_English_contractions

Chat Words Conversion

Chat words conversion, also known as text messaging or SMS language conversion, refers to the process of transforming abbreviations, acronyms, and shortcuts commonly used in online chat, text messages, and social media into their full and grammatically correct forms. This conversion is essential for natural language processing (NLP) and text analysis tasks, as chat words often deviate from standard written language rules. By converting chat words, the text becomes more formal, consistent, and suitable for various NLP applications.

Spelling Checking and Correction

Spelling checking and correction is a crucial aspect of natural language processing (NLP) and text analysis. It involves the automatic detection and correction of spelling errors in text. Accurate spelling is essential for effective communication, comprehension, and analysis of text data. Spelling errors can occur due to typos, keyboard mistakes, or improper language usage.

Separate Combined Words

In natural language processing (NLP), the term “separate combined words” refers to the task of splitting or separating words that are combined or written together without spaces or other delimiters.
Combined words can occur due to various reasons, such as typos, abbreviations, or specific linguistic phenomena. The goal of separating combined words is to identify individual words within the combined sequence.

Remove Stopwords

Removing stopwords is a common text preprocessing technique used in natural language processing (NLP) and text analysis tasks. Stopwords are common words that occur frequently in a language and do not carry significant meaning or context. Examples of stopwords in English include “the,” “and,” “is,” “in,” “a,” “an,” etc. Removing stopwords is beneficial in various NLP applications to reduce noise and improve the efficiency and accuracy of text analysis.

Stemming

Stemming is a text normalization technique used in natural language processing (NLP) to reduce words to their base or root form, known as the “stem.” The process involves removing suffixes or prefixes from words to obtain the core meaning or base representation of a word. The stemmed words are not always actual words or meaningful on their own, but they help in reducing word variations and simplifying text analysis tasks.

Lemmatization

Lemmatization is a text normalization technique used in natural language processing (NLP) to reduce words to their base or root form, known as the “lemma.” Unlike stemming, which simply removes suffixes or prefixes, lemmatization considers the context and meaning of words to produce meaningful base forms. The resulting lemmas are actual words found in a dictionary, making them more interpretable and linguistically correct.

Remove Punctuations

Removing punctuations is a common text preprocessing step in natural language processing (NLP) and text analysis tasks. Punctuations are symbols used in writing to separate sentences, indicate pauses, or convey specific meanings. Examples of punctuations include periods (.), commas (,), exclamation marks (!), question marks (?), and more.

Remove Numbers and Extra Spaces

Removing numbers and extra spaces is a text preprocessing step commonly performed in natural language processing (NLP) and text analysis tasks. The process involves eliminating numeric digits and any unnecessary or excessive spaces from the text data. Removing numbers and extra spaces can help improve the quality and efficiency of text processing and analysis.

GitHub Repository

Text-Preprocessing-Pipeline-V2

Notion Article (More Detailed)

Text-Preprocessing-Pipeline-V2–94b809db258e4c418615d5c1a07445d3

Connect Me

https://www.linkedin.com/in/kishan-tongrao-6b9201112

Thank You!

Originally published at https://kishantongrao.notion.site.