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!
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.
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