This repository contains code for preprocessing natural language data for use in NLP applications.The following preprocessing techniques are implemented:
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Tokenization: Breaking text into individual words, phrases, symbols, or other meaningful elements called tokens.
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Stop word removal: Removing words that occur frequently in a language and are unlikely to carry any useful information for text classification.
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Lemmatization: Similar to stemming, but reducing words to their dictionary or canonical form, e.g., "am", "are", "is" to "be".
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Abbreviation decoding: Replacing common abbreviations with their full forms, e.g., "Mr." to "Mister".
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Emoticon/emoji replacement: Replacing emoticons and emojis with their corresponding text representation, e.g., "😊" to ":)".
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Hashtag, HTML tag, mention, punctuation, number, and URL removal: Removing all the hashtags, HTML tags, mentions, punctuations, numbers, and URLs from the text.
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Lowercasing: Converting all the text to lowercase.
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Spellchecking: Correcting misspelled words in the text.
The preprocessing techniques are implemented using Python and various NLP libraries, such as NLTK, spaCy, BeautifulSoup, emoji, and gensim. The code is provided as Google Colab, which demonstrate how to preprocess text data using these techniques.
The preprocessed data can be used for a variety of NLP tasks, such as sentiment analysis, topic modeling, and text classification. The code is open source and can be used for both academic and commercial purposes.
I'm a data scientist with a specialization in Natural Language Processing (NLP). I have experience working on NLP projects and conducting research in this field.
As an NLP researcher, I have expertise in a variety of NLP techniques such as text classification, sentiment analysis, named entity recognition, and text summarization.
