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[LLMOps](https://www.analyticsvidhya.com/blog/category/llmops/?ref=category) [Python](https://www.analyticsvidhya.com/blog/category/python/?ref=category) [NLP](https://www.analyticsvidhya.com/blog/category/nlp/?ref=category) [SQL](https://www.analyticsvidhya.com/blog/category/sql/?ref=category) [AIML Projects](https://www.analyticsvidhya.com/blog/category/project/?ref=category) #### Reading list ##### Introduction to NLP [What is NLP?](https://www.analyticsvidhya.com/blog/2017/01/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python/)[Applications of NLP](https://www.analyticsvidhya.com/blog/2020/07/top-10-applications-of-natural-language-processing-nlp/) ##### Text Pre-processing [Understanding Text Pre-processing](https://www.analyticsvidhya.com/blog/2021/06/text-preprocessing-in-nlp-with-python-codes/)[Tokenization in NLP](https://www.analyticsvidhya.com/blog/2019/07/how-get-started-nlp-6-unique-ways-perform-tokenization/)[Byte Pair Encoding](https://www.analyticsvidhya.com/blog/2020/05/what-is-tokenization-nlp/)[Tokenizer Free Language Modeling with Pixels](https://www.analyticsvidhya.com/blog/2022/09/tokenizer-free-language-modeling-with-pixels/)[Stopword Removal](https://www.analyticsvidhya.com/blog/2019/08/how-to-remove-stopwords-text-normalization-nltk-spacy-gensim-python/)[Stemming vs Lemmatization](https://www.analyticsvidhya.com/blog/2022/06/stemming-vs-lemmatization-in-nlp-must-know-differences/)[Text Mining](https://www.analyticsvidhya.com/blog/2021/05/how-to-build-word-cloud-in-python/) ##### NLP Libraries [Spacy Tutorials](https://www.analyticsvidhya.com/blog/2020/03/spacy-tutorial-learn-natural-language-processing/)[Gensim Tutorials](https://www.analyticsvidhya.com/blog/2022/02/topic-identification-with-gensim-library-using-python/) ##### Regular Expressions [What are Regular Expressions?](https://www.analyticsvidhya.com/blog/2021/06/regex-cheatsheet-for-natural-language-processing-tasks/)[Regular Expressions](https://www.analyticsvidhya.com/blog/2020/01/4-applications-of-regular-expressions-that-every-data-scientist-should-know-with-python-code/) ##### String Similarity [String Similarity](https://www.analyticsvidhya.com/blog/2021/07/fuzzy-string-matching-a-hands-on-guide/) ##### Spelling Correction [Spelling Correction](https://www.analyticsvidhya.com/blog/2021/11/autocorrect-feature-using-nlp-in-python/) ##### Topic Modeling [Introduction to Topic Modeling](https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/)[Latent Dirichlet Allocation (LDA)](https://www.analyticsvidhya.com/blog/2021/06/part-2-topic-modeling-and-latent-dirichlet-allocation-lda-using-gensim-and-sklearn/)[Implement Topic Modeling](https://www.analyticsvidhya.com/blog/2022/08/supervised-topic-models/) ##### Text Representation [Introduction to Feature Engineering for Text Data](https://www.analyticsvidhya.com/blog/2021/04/a-guide-to-feature-engineering-in-nlp/)[Implement Text Feature Engineering Techniques](https://www.analyticsvidhya.com/blog/2015/10/6-practices-enhance-performance-text-classification-model/)[Introduction to One Hot Encoding](https://www.analyticsvidhya.com/blog/2020/03/one-hot-encoding-vs-label-encoding-using-scikit-learn/)[Implement One Hot Encoding](https://www.analyticsvidhya.com/blog/2021/05/how-to-perform-one-hot-encoding-for-multi-categorical-variables/)[Limitations of One Hot Encoding](https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding/)[Count Vectorizer and TF-IDF](https://www.analyticsvidhya.com/blog/2021/06/part-5-step-by-step-guide-to-master-nlp-text-vectorization-approaches/)[Solving Text classification using TF-IDF](https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/) ##### Information Retrieval System [Information Retrieval System Explained in Simple terms\!](https://www.analyticsvidhya.com/blog/2020/06/nlp-project-information-extraction/)[How does Google Rank Search Results?](https://www.analyticsvidhya.com/blog/2015/04/pagerank-explained-simple/)[Knowledge Graph](https://www.analyticsvidhya.com/blog/2019/10/how-to-build-knowledge-graph-text-using-spacy/) ##### Word Vectors [Understanding Word2Vec](https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/)[Understanding Skip Gram and Continous Bag Of Words](https://www.analyticsvidhya.com/blog/2020/03/pretrained-word-embeddings-nlp/)[Word2Vec Implementation in Gensim](https://www.analyticsvidhya.com/blog/2021/06/practical-guide-to-word-embedding-system/)[Visualizing Word2Vec](https://www.analyticsvidhya.com/blog/2019/07/how-to-build-recommendation-system-word2vec-python/) ##### Word Senses [Word Senses and Word Sense Ambiguity](https://www.analyticsvidhya.com/blog/2021/06/word-sense-disambiguation-importance-in-natural-language-processing/) ##### Dependency Parsing [Why Are We Interested in Syntatic Strucure?](https://www.analyticsvidhya.com/blog/2021/06/part-11-step-by-step-guide-to-master-nlp-syntactic-analysis/)[What is a Dependency Grammar?](https://www.analyticsvidhya.com/blog/2020/07/part-of-speechpos-tagging-dependency-parsing-and-constituency-parsing-in-nlp/)[Neural Dependency Parsing](https://www.analyticsvidhya.com/blog/2019/02/stanfordnlp-nlp-library-python/) ##### Language Modeling [Introduction to Language Models](https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-language-model-nlp-python-code/)[N-Gram Language Models](https://www.analyticsvidhya.com/blog/2021/09/what-are-n-grams-and-how-to-implement-them-in-python/)[Neural Language models](https://www.analyticsvidhya.com/blog/2020/08/build-a-natural-language-generation-nlg-system-using-pytorch/) ##### Getting Started with RNN [Why Sequence models?](https://www.analyticsvidhya.com/blog/2019/01/sequence-models-deeplearning/)[Usecases of Sequence models](https://www.analyticsvidhya.com/blog/2018/03/essentials-of-deep-learning-sequence-to-sequence-modelling-with-attention-part-i/)[Introduction to RNN](https://www.analyticsvidhya.com/blog/2022/03/a-brief-overview-of-recurrent-neural-networks-rnn/)[Implement RNN](https://www.analyticsvidhya.com/blog/2022/01/tutorial-on-rnn-lstm-gru-with-implementation/) ##### Different Variants of RNN [Shortcomings of RNN](https://www.analyticsvidhya.com/blog/2021/06/a-visual-guide-to-recurrent-neural-networks/)[What is Long Short Term Memory (LSTM)](https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/)[Implementing LSTM](https://www.analyticsvidhya.com/blog/2021/06/lstm-for-text-classification/)[Build Your Own Fake News Classification Model](https://www.analyticsvidhya.com/blog/2021/07/detecting-fake-news-with-natural-language-processing/)[What is Gated Recurrent Unit (GRU)?](https://www.analyticsvidhya.com/blog/2021/03/introduction-to-gated-recurrent-unit-gru/)[Implementing GRU](https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/) ##### Machine Translation and Attention [Introduction to Machine Translation](https://www.analyticsvidhya.com/blog/2019/01/neural-machine-translation-keras/)[Multilingualism in NLP](https://www.analyticsvidhya.com/blog/2020/01/3-important-nlp-libraries-indian-languages-python/)[Drawbacks of Seq2Seq model](https://www.analyticsvidhya.com/blog/2020/08/a-simple-introduction-to-sequence-to-sequence-models/)[Mathematical Calculation of Attention](https://www.analyticsvidhya.com/blog/2019/11/comprehensive-guide-attention-mechanism-deep-learning/) ##### Self Attention and Transformers [Understand Positional Encoding](https://www.analyticsvidhya.com/blog/2021/09/an-explanatory-guide-to-bert-tokenizer/)[Introducing Transformers Model](https://www.analyticsvidhya.com/blog/2019/06/understanding-transformers-nlp-state-of-the-art-models/)[Key Query Value Attention in Tranformer Encoder](https://www.analyticsvidhya.com/blog/2021/01/implementation-of-attention-mechanism-for-caption-generation-on-transformers-using-tensorflow/) ##### Transfomers and Pretraining [Pretrained Language Models in NLP](https://www.analyticsvidhya.com/blog/2019/03/pretrained-models-get-started-nlp/)[Generative Pre-training (GPT) for Natural Language Understanding(NLU)](https://www.analyticsvidhya.com/blog/2021/09/building-a-machine-learning-model-for-title-generation/)[Finetuning GPT-2](https://www.analyticsvidhya.com/blog/2019/07/openai-gpt2-text-generator-python/)[Understanding BERT](https://www.analyticsvidhya.com/blog/2019/09/demystifying-bert-groundbreaking-nlp-framework/)[Finetune Masked language Modeling in BERT](https://www.analyticsvidhya.com/blog/2021/12/fine-tune-bert-model-for-sentiment-analysis-in-google-colab/)[Implement Text Classification using BERT](https://www.analyticsvidhya.com/blog/2020/07/transfer-learning-for-nlp-fine-tuning-bert-for-text-classification/)[Finetuning BERT for NER](https://www.analyticsvidhya.com/blog/2022/06/fine-tune-bert-model-for-named-entity-recognition-in-google-colab/)[Extensions of BERT: Roberta, Spanbert, ALBER](https://www.analyticsvidhya.com/blog/2022/10/albert-model-for-self-supervised-learning/)[MobileBERT](https://www.analyticsvidhya.com/blog/2020/07/mobilebert/)[GPT-3](https://www.analyticsvidhya.com/blog/2021/05/hands-on-experience-with-gpt3/)[Prompt Engineering in GPT-3](https://www.analyticsvidhya.com/blog/2022/05/prompt-engineering-in-gpt-3/)[Bigbird](https://www.analyticsvidhya.com/blog/2022/11/an-introduction-to-bigbird/)[T5 and large language models](https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/) ##### Question Answering [Implement Question Answering on SQUAD](https://www.analyticsvidhya.com/blog/2021/11/end-to-end-question-answering-system-using-nlp-and-squad-dataset/) ##### Text Summarization [Text Summarization](https://www.analyticsvidhya.com/blog/2019/06/comprehensive-guide-text-summarization-using-deep-learning-python/) ##### Named Entity Recognition [Named Entity Recognition (NER) in Python with Spacy](https://www.analyticsvidhya.com/blog/2021/06/nlp-application-named-entity-recognition-ner-in-python-with-spacy/) ##### Coreference Resolution [Coreference Resolution](https://www.analyticsvidhya.com/blog/2021/07/new-anaphora-and-co-reference-resolution-technique-for-biographies/) ##### Audio Data [Visualizing Sounds Using Librosa Machine Learning Library\!](https://www.analyticsvidhya.com/blog/2021/06/visualizing-sounds-librosa/)[Audio Processing](https://www.analyticsvidhya.com/blog/2017/08/audio-voice-processing-deep-learning/)[Audio Analysis](https://www.analyticsvidhya.com/blog/2022/01/analysis-of-zero-crossing-rates-of-different-music-genre-tracks/)[Audio Classification using Deep Learning](https://www.analyticsvidhya.com/blog/2020/01/how-to-perform-automatic-music-generation/) ##### ASR [Automatic Speech Recognition](https://www.analyticsvidhya.com/blog/2021/01/introduction-to-automatic-speech-recognition-and-natural-language-processing/)[Implement Automatic Speech Recognition](https://www.analyticsvidhya.com/blog/2019/07/learn-build-first-speech-to-text-model-python/)[Can Voice Conversion Improve ASR in Low-Resource Settings?](https://www.analyticsvidhya.com/blog/2022/09/can-voice-conversion-improve-asr-in-low-resource-settings/) ##### Audio Separation [Audio Separation](https://www.analyticsvidhya.com/blog/2021/08/speech-separation-by-facebook-ai-research/) ##### Chatbot [Building Chatbots](https://www.analyticsvidhya.com/blog/2021/12/creating-chatbot-building-using-python/)[Building Chatbots using Rasa](https://www.analyticsvidhya.com/blog/2019/04/learn-build-chatbot-rasa-nlp-ipl/) ##### Auto NLP [Automate NLP Tasks using EvalML Library](https://www.analyticsvidhya.com/blog/2021/04/automate-nlp-tasks-using-evalml-library/) 1. [Home](https://www.analyticsvidhya.com/blog/) 2. [Advanced](https://www.analyticsvidhya.com/blog/category/advanced/) 3. Natural Language Processing: Step by Step Guide # Natural Language Processing: Step by Step Guide [](https://www.analyticsvidhya.com/blog/author/amruta99/) [Amruta](https://www.analyticsvidhya.com/blog/author/amruta99/) Last Updated : 26 Feb, 2024 7 min read 4 ## **Introduction** NLP stands for Natural Language Processing, a part of Computer Science, Human Language, and Artificial Intelligence. This technology is used by computers to understand, analyze, manipulate, and interpret human languages. NLP algorithms, leveraged by data scientists and machine learning professionals, are widely used everywhere in areas like Gmail spam, any search, games, and many more. These algorithms employ techniques such as neural networks to process and interpret text, enabling tasks like sentiment analysis, document classification, and information retrieval. Not only that, today we have build complex deep learning architectures like transformers which are used to build language models that are the core behind GPT, **[Gemini](https://www.analyticsvidhya.com/blog/2023/12/what-is-google-gemini-features-usage-and-limitations/)**, and the likes. #### **Learning Objective** - Basic understanding of Natural Language Processing. - Learn Various Techniques used for the implementation of NLP. - Understand how to use NLP for text mining. ***This article was published as a part of the*** [***Data Science Blogatho***](https://datahack.analyticsvidhya.com/blogathon/)[***n***](https://datahack.analyticsvidhya.com/blogathon/) ## Table of contents 1. [Why NLP is so important?](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-why-nlp-is-so-important) 2. [Components of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-components-of-nlp) - [Natural Language Understanding](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-natural-language-understanding) - [Natural Language Generation](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-natural-language-generation) 3. [Phases of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-phases-of-nlp-nbsp) - [Lexical Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-lexical-analysis) - [Syntactic Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-syntactic-analysis) - [Semantic Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-semantic-analysis) - [Discourse Integration](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-discourse-integration) - [Pragmatic Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-pragmatic-analysis) 4. [Implementation of NLP using Python](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-implementation-of-nlp-using-python) 5. [Advantages of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-advantages-of-nlp) 6. [Disadvantages of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-disadvantages-of-nlp) 7. [Everyday NLP examples](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-everyday-nlp-examples) 8. [Frequently Asked Questions](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#faq) [Free Certification Courses Introduction to Transformers & Attention Understand RNN basics • Build & tune LSTM/GRU models • Encoder-decoder flow Get Certified Now](https://www.analyticsvidhya.com/courses/introduction-to-transformers-and-attention-mechanisms/?utm_source=blog&utm_medium=banner) ## **Why NLP is so important?** #### **Text data in a massive amount** NLP helps machines to interact with humans in their language and perform related tasks like reading text, understand speech and interpret it in well format. Nowadays machines can analyze more data rather than humans efficiently. All of us know that every day plenty amount of data is generated from various fields such as the medical and pharma industry, social media like Facebook, Instagram, etc. And this data is not well structured (i.e. unstructured) so it becomes a tedious job, that’s why we need NLP. We need NLP for tasks like sentiment analysis, machine translation, **[POS](https://www.analyticsvidhya.com/blog/2020/07/part-of-speechpos-tagging-dependency-parsing-and-constituency-parsing-in-nlp/)** tagging or part-of-speech tagging , named entity recognition, creating chatbots, comment segmentation, question answering, etc. #### **Unstructured data to structured** We know that supervised and **[unsupervised learning](https://www.analyticsvidhya.com/blog/2020/04/supervised-learning-unsupervised-learning/)** and deep learning are now extensively used to manipulate human language. That’s why we need a proper understanding of the text. I am going to explain this understanding in this article.NLP is very important to get exact or useful insights from text. Meaningful information is gathered ## **Components of NLP** NLP is divided into two components. - **Natural Language Understanding** - **Natural Language Generation**  ### **Natural Language Understanding** Natural Language Understanding (NLU) helps the machine to understand and analyze human language by extracting the text from large data such as keywords, emotions, relations, and semantics, etc. Let’s see what challenges are faced by a machine- *For Example:-* - *He is looking for a match.* What do you understand by the ‘match’ keyword? Does it partner or cricket or football or anything else? This is **Lexical Ambiguity.** It happens when a word has different meanings. Lexical ambiguity can be resolved by using parts-of-speech (POS)tagging techniques. - *The Fish is ready to eat.* What do you understand by the above example? Is the fish ready to eat his/her food or fish is ready for someone to eat? Got confused!! Right? We will see it practically below. This is ***[Syntactical Ambiguity](https://www.analyticsvidhya.com/blog/2021/06/part-11-step-by-step-guide-to-master-nlp-syntactic-analysis/)*** which means when we see more meanings in a sequence of words and also Called Grammatical Ambiguity. ### **Natural Language Generation** It is the process of extracting meaningful insights as phrases and sentences in the form of natural language. It consists − - **Text planning −** It includes retrieving the relevant data from the domain. - **Sentence planning −** It is nothing but a selection of important words, meaningful phrases, or sentences. ## **Phases of NLP**  ### **Lexical Analysis** It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in that particular language. The lexical analysis divides the text into paragraphs, sentences, and words. So we need to perform **[Lexicon Normalization](https://www.analyticsvidhya.com/blog/2021/03/tokenization-and-text-normalization/)**. The most common lexicon normalization techniques are Stemming: - Stemming: Stemming is the process of reducing derived words to their word stem, base, or root form—generally a written word form like-“ing”, “ly”, “es”, “s”, etc - Lemmatization: Lemmatization is the process of reducing a group of words into their lemma or dictionary form. It takes into account things like POS(Parts of Speech), the meaning of the word in the sentence, the meaning of the word in the nearby sentences, etc. before reducing the word to its lemma. ### **Syntactic Analysis** Syntactic Analysis is used to check grammar, arrangements of words, and the interrelationship between the words. **Example:** Mumbai goes to the Sara Here “Mumbai goes to Sara”, which does not make any sense, so this sentence is rejected by the Syntactic analyzer. Syntactical parsing involves the analysis of words in the sentence for grammar. Dependency Grammar and Part of Speech (POS)tags are the important attributes of text syntactic. ### **Semantic Analysis** Retrieves the possible meanings of a sentence that is clear and semantically correct. Its process of retrieving meaningful insights from text. ### **Discourse Integration** It is nothing but a sense of context. That is sentence or word depends upon that sentences or words. It’s like the use of proper nouns/pronouns. For example, Ram wants it. In the above statement, we can clearly see that the “it” keyword does not make any sense. In fact, it is referring to anything that we don’t know. That is nothing but this “it” word depends upon the previous sentence which is not given. So once we get to know about “it”, we can easily find out the reference. ### **Pragmatic Analysis** It means the study of meanings in a given language. Process of extraction of insights from the text. It includes the repetition of words, who said to whom? etc. It understands that how people communicate with each other, in which context they are talking and so many aspects. Okay! .. So at this point, we came to know that all the basic concepts of NLP. Here we will discuss all these points practically …so let’s move on\! ## **Implementation of NLP using Python** I am going to show you how to perform NLP using Python. Python is very simple, easy to understand and interpret. First, we will import all necessary libraries as shown below. We will be working with the NLTK library but there is also the spacy library for this. ``` Copy Code ``` In the above code, we have imported libraries such as **pandas** to deal with data frames/datasets, **re** for regular expression, **nltk** is a natural language tool kit in which we have imported modules like **stopwords** which is nothing but “dictionary” and **PorterStemmer** to generate root word. ``` Copy Code ``` Here we have read the file named “Women’s Clothing E-Commerce Reviews” in CSV(comma-separated value) format. And also checked for null values. You can find this dataset on this link: ``` Copy Code ``` Further, we will perform some data visualizations using matplotlib and seaborn libraries which are really the best visualization libraries in Python. I have taken only one graph, you can perform more graphs to see how your data is\! ``` Copy Code ``` From nltk library, we have to download stopwords for text cleaning. ``` Copy Code ``` Here we will perform all operations of data cleaning such as lemmatization, stemming, etc to get pure data. ``` Copy Code ``` Now it’s time to see how many positive words are there in “Reviews” from the dataset by using the above code. ``` Copy Code ``` Now it’s time to see how many negative words are there in “Reviews” from the dataset by using the above code. ``` Copy Code ``` By using the above code, we can simply show the word cloud of the most common words in the Reviews column in the dataset. So, Finally, we have done all concepts with theory and implementation of NLP in Python…..\! ## **Advantages of NLP** - Removes unnecessary information. - NLP helps computers to interact with humans in their languages ## **Disadvantages of NLP** - NLP may not show full context. - NLP is unpredictable sometimes. ## **Everyday NLP examples** There are many common day-to-day life applications of NLP. Apart from virtual assistants like Alexa or Siri, here are a few more examples you can see. - Email filtering. Spam messages whose content is malicious get automatically filtered by the Gmail system and put into the spam folder. - Autocorrection of any text by using techniques of NLP. Sometimes we see that in mobile chat application or google search our word/sentence get automatically autocorrected. This is because of NLP. - Text classification of tweets or reviews whether they are talking positively or negatively in the text. ## **Conclusion** In this tutorial for beginners we understood that NLP, or Natural Language Processing, enables computers to understand human languages through algorithms like sentiment analysis and document classification. Using NLP, fundamental deep learning architectures like transformers power advanced language models such as ChatGPT. Therefore, proficiency in NLP is crucial for innovation and customer understanding, addressing challenges like lexical and syntactic ambiguity. Python programming language, often used for NLP tasks, includes NLP techniques like preprocessing text with libraries like NLTK for data cleaning. Given the power of NLP, it is used in various applications like text summarization, open source language models, text retrieval in search engines, etc. demonstrating its pervasive impact in modern technology. #### Key Takeaways - NLP (Natural Language Processing) revolutionizes human-computer interaction, enabling machines to understand and interpret human languages effectively. - NLP encompasses Natural Language Understanding (NLU) and Generation (NLG), addressing challenges like lexical and syntactic ambiguity for accurate interpretation and generation of text. - Python serves as a fundamental tool for NLP implementation, offering libraries like NLTK for text preprocessing and data cleaning. - NLP finds extensive real-world applications including email filtering, autocorrection, and text classification, driving innovation and automation across industries. ***The media shown in this article on Natural Language Processing are not owned by Analytics Vidhya and is used at the Author’s discretion.*** [](https://www.analyticsvidhya.com/blog/author/amruta99/) [Amruta](https://www.analyticsvidhya.com/blog/author/amruta99/) I am Software Engineer, data enthusiast , passionate about data and its potential to drive insights, solve problems and also seeking to learn more about machine learning, artificial intelligence fields. [Advanced](https://www.analyticsvidhya.com/blog/category/advanced/)[NLP](https://www.analyticsvidhya.com/blog/category/nlp/)[Python](https://www.analyticsvidhya.com/blog/category/python-2/)[Python](https://www.analyticsvidhya.com/blog/category/python/)[Structured Data](https://www.analyticsvidhya.com/blog/category/structured-data/)[Text](https://www.analyticsvidhya.com/blog/category/text/)[Unsupervised](https://www.analyticsvidhya.com/blog/category/unsupervised/) #### Login to continue reading and enjoy expert-curated content. Keep Reading for Free ## Free Courses [ 5 Build a Document Retriever Search Engine with LangChain ​Learn to create a document retrieval search engine using LangChain. ​](https://www.analyticsvidhya.com/courses/build-a-document-retriever-search-engine-with-langchain/?utm_source=blog&utm_medium=free_course_recommendation) [ 4.6 Coding a ChatGPT-style Language Model From Scratch in Pytorch Build a ChatGPT-style language model using PyTorch.](https://www.analyticsvidhya.com/courses/coding-a-chatgpt-style-language-model-from-scratch-in-pytorch/?utm_source=blog&utm_medium=free_course_recommendation) [ 4.5 Naive Bayes from Scratch Master Naïve Bayes for ML: Build classifiers, analyze data, and apply Bayes.](https://www.analyticsvidhya.com/courses/naive-bayes/?utm_source=blog&utm_medium=free_course_recommendation) #### Recommended Articles - [GPT-4 vs. Llama 3.1 – Which Model is Better?](https://www.analyticsvidhya.com/blog/2024/08/gpt-4-vs-llama-3-1/) - [Llama-3.1-Storm-8B: The 8B LLM Powerhouse Surpa...](https://www.analyticsvidhya.com/blog/2024/08/llama-3-1-storm-8b/) - [A Comprehensive Guide to Building Agentic RAG S...](https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/) - [Top 10 Machine Learning Algorithms in 2026](https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/) - [45 Questions to Test a Data Scientist on Basics...](https://www.analyticsvidhya.com/blog/2017/01/must-know-questions-deep-learning/) - [90+ Python Interview Questions and Answers (202...](https://www.analyticsvidhya.com/blog/2022/07/python-coding-interview-questions-for-freshers/) - [8 Easy Ways to Access ChatGPT for Free](https://www.analyticsvidhya.com/blog/2023/12/chatgpt-4-for-free/) - [Prompt Engineering: Definition, Examples, Tips ...](https://www.analyticsvidhya.com/blog/2023/06/what-is-prompt-engineering/) - [What is LangChain?](https://www.analyticsvidhya.com/blog/2024/06/langchain-guide/) - [What is Retrieval-Augmented Generation (RAG)?](https://www.analyticsvidhya.com/blog/2023/09/retrieval-augmented-generation-rag-in-ai/) ### Responses From Readers [Cancel reply](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#respond)  Nirbhay Rana Amruta could you please direct me to the good study material for the NLU and NLG. I have deep interest in this field but not able to find any good content on these. 123 [Cancel reply](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#respond)  sg You have forgotten to include definitions of Negative and Positive dataframes, otherwise its a good article 123 [Cancel reply](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#respond) ### Frequently Asked Questions ## Q1. What are the 5 steps of natural language processing? A. Preprocessing involves cleaning and tokenizing text data. Word embedding converts words into numerical vectors. Dependency parsing analyzes grammatical structure. Modeling employs machine learning algorithms for predictive tasks. Evaluation assesses model performance using metrics like those provided by Microsoft’s NLP models. ## Q2. How do I start learning natural language processing? A. 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## **Introduction** NLP stands for Natural Language Processing, a part of Computer Science, Human Language, and Artificial Intelligence. This technology is used by computers to understand, analyze, manipulate, and interpret human languages. NLP algorithms, leveraged by data scientists and machine learning professionals, are widely used everywhere in areas like Gmail spam, any search, games, and many more. These algorithms employ techniques such as neural networks to process and interpret text, enabling tasks like sentiment analysis, document classification, and information retrieval. Not only that, today we have build complex deep learning architectures like transformers which are used to build language models that are the core behind GPT, **[Gemini](https://www.analyticsvidhya.com/blog/2023/12/what-is-google-gemini-features-usage-and-limitations/)**, and the likes. #### **Learning Objective** - Basic understanding of Natural Language Processing. - Learn Various Techniques used for the implementation of NLP. - Understand how to use NLP for text mining. ***This article was published as a part of the*** [***Data Science Blogatho***](https://datahack.analyticsvidhya.com/blogathon/)[***n***](https://datahack.analyticsvidhya.com/blogathon/) 1. [Why NLP is so important?](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-why-nlp-is-so-important) 2. [Components of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-components-of-nlp) - [Natural Language Understanding](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-natural-language-understanding) - [Natural Language Generation](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-natural-language-generation) 3. [Phases of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-phases-of-nlp-nbsp) - [Lexical Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-lexical-analysis) - [Syntactic Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-syntactic-analysis) - [Semantic Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-semantic-analysis) - [Discourse Integration](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-discourse-integration) - [Pragmatic Analysis](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-pragmatic-analysis) 4. [Implementation of NLP using Python](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-implementation-of-nlp-using-python) 5. [Advantages of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-advantages-of-nlp) 6. [Disadvantages of NLP](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-disadvantages-of-nlp) 7. [Everyday NLP examples](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#h-everyday-nlp-examples) 8. [Frequently Asked Questions](https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/#faq) ## **Why NLP is so important?** #### **Text data in a massive amount** NLP helps machines to interact with humans in their language and perform related tasks like reading text, understand speech and interpret it in well format. Nowadays machines can analyze more data rather than humans efficiently. All of us know that every day plenty amount of data is generated from various fields such as the medical and pharma industry, social media like Facebook, Instagram, etc. And this data is not well structured (i.e. unstructured) so it becomes a tedious job, that’s why we need NLP. We need NLP for tasks like sentiment analysis, machine translation, **[POS](https://www.analyticsvidhya.com/blog/2020/07/part-of-speechpos-tagging-dependency-parsing-and-constituency-parsing-in-nlp/)** tagging or part-of-speech tagging , named entity recognition, creating chatbots, comment segmentation, question answering, etc. #### **Unstructured data to structured** We know that supervised and **[unsupervised learning](https://www.analyticsvidhya.com/blog/2020/04/supervised-learning-unsupervised-learning/)** and deep learning are now extensively used to manipulate human language. That’s why we need a proper understanding of the text. I am going to explain this understanding in this article.NLP is very important to get exact or useful insights from text. Meaningful information is gathered ## **Components of NLP** NLP is divided into two components. - **Natural Language Understanding** - **Natural Language Generation**  ### **Natural Language Understanding** Natural Language Understanding (NLU) helps the machine to understand and analyze human language by extracting the text from large data such as keywords, emotions, relations, and semantics, etc. Let’s see what challenges are faced by a machine- *For Example:-* - *He is looking for a match.* What do you understand by the ‘match’ keyword? Does it partner or cricket or football or anything else? This is **Lexical Ambiguity.** It happens when a word has different meanings. Lexical ambiguity can be resolved by using parts-of-speech (POS)tagging techniques. - *The Fish is ready to eat.* What do you understand by the above example? Is the fish ready to eat his/her food or fish is ready for someone to eat? Got confused!! Right? We will see it practically below. This is ***[Syntactical Ambiguity](https://www.analyticsvidhya.com/blog/2021/06/part-11-step-by-step-guide-to-master-nlp-syntactic-analysis/)*** which means when we see more meanings in a sequence of words and also Called Grammatical Ambiguity. ### **Natural Language Generation** It is the process of extracting meaningful insights as phrases and sentences in the form of natural language. It consists − - **Text planning −** It includes retrieving the relevant data from the domain. - **Sentence planning −** It is nothing but a selection of important words, meaningful phrases, or sentences. ## **Phases of NLP**  ### **Lexical Analysis** It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in that particular language. The lexical analysis divides the text into paragraphs, sentences, and words. So we need to perform **[Lexicon Normalization](https://www.analyticsvidhya.com/blog/2021/03/tokenization-and-text-normalization/)**. The most common lexicon normalization techniques are Stemming: - Stemming: Stemming is the process of reducing derived words to their word stem, base, or root form—generally a written word form like-“ing”, “ly”, “es”, “s”, etc - Lemmatization: Lemmatization is the process of reducing a group of words into their lemma or dictionary form. It takes into account things like POS(Parts of Speech), the meaning of the word in the sentence, the meaning of the word in the nearby sentences, etc. before reducing the word to its lemma. ### **Syntactic Analysis** Syntactic Analysis is used to check grammar, arrangements of words, and the interrelationship between the words. **Example:** Mumbai goes to the Sara Here “Mumbai goes to Sara”, which does not make any sense, so this sentence is rejected by the Syntactic analyzer. Syntactical parsing involves the analysis of words in the sentence for grammar. Dependency Grammar and Part of Speech (POS)tags are the important attributes of text syntactic. ### **Semantic Analysis** Retrieves the possible meanings of a sentence that is clear and semantically correct. Its process of retrieving meaningful insights from text. ### **Discourse Integration** It is nothing but a sense of context. That is sentence or word depends upon that sentences or words. It’s like the use of proper nouns/pronouns. For example, Ram wants it. In the above statement, we can clearly see that the “it” keyword does not make any sense. In fact, it is referring to anything that we don’t know. That is nothing but this “it” word depends upon the previous sentence which is not given. So once we get to know about “it”, we can easily find out the reference. ### **Pragmatic Analysis** It means the study of meanings in a given language. Process of extraction of insights from the text. It includes the repetition of words, who said to whom? etc. It understands that how people communicate with each other, in which context they are talking and so many aspects. Okay! .. So at this point, we came to know that all the basic concepts of NLP. Here we will discuss all these points practically …so let’s move on\! ## **Implementation of NLP using Python** I am going to show you how to perform NLP using Python. Python is very simple, easy to understand and interpret. First, we will import all necessary libraries as shown below. We will be working with the NLTK library but there is also the spacy library for this. ``` # Importing the libraries import pandas as pd import re from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer ``` In the above code, we have imported libraries such as **pandas** to deal with data frames/datasets, **re** for regular expression, **nltk** is a natural language tool kit in which we have imported modules like **stopwords** which is nothing but “dictionary” and **PorterStemmer** to generate root word. ``` df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv',header=0,index_col=0) df.head() # Null Entries df.isna().sum() ``` Here we have read the file named “Women’s Clothing E-Commerce Reviews” in CSV(comma-separated value) format. And also checked for null values. You can find this dataset on this link: ``` import matplotlib.pyplot as plt import seaborn as sns sns.countplot(x='Rating',data=df_temp) plt.title("Distribution of Rating") ``` Further, we will perform some data visualizations using matplotlib and seaborn libraries which are really the best visualization libraries in Python. I have taken only one graph, you can perform more graphs to see how your data is\! ``` nltk.download('stopwords') stops=stopwords.words("english") ``` From nltk library, we have to download stopwords for text cleaning. ``` review=df_temp[['Review','Recommended']] pd.DataFrame(review) def tokens(words): words = re.sub("[^a-zA-Z]"," ", words) text = words.lower().split() return " ".join(text) review['Review_clear'] = review['Review'].apply(tokens) review.head() corpus=[] for i in range(0,22628): Review=re.sub("[^a-zA-Z]"," ", df_temp["Review"][i]) Review=Review.lower() Review=Review.split() ps=PorterStemmer() Review=[ps.stem(word) for word in Review if not word in set(stops)] tocken=" ".join(Review) corpus.append(tocken) ``` Here we will perform all operations of data cleaning such as lemmatization, stemming, etc to get pure data. ``` positive_words =[] for i in positive.Review_clear: positive_words.append(i) positive_words = ' '.join(positive_words) positive_words ``` Now it’s time to see how many positive words are there in “Reviews” from the dataset by using the above code. ``` negative_words = [] for j in Negative.Review_clear: negative_words.append(j) negative_words = ' '.join(negative_words) negative_words ``` Now it’s time to see how many negative words are there in “Reviews” from the dataset by using the above code. ``` # Library for WordCloud from wordcloud import WordCloud import matplotlib.pyplot as plt wordcloud = WordCloud(background_color="white", max_words=len(negative_words)) wordcloud.generate(positive_words) plt.figure(figsize=(13,13)) plt.imshow(wordcloud, interpolation="bilinear") plt.axis("off") plt.show() ``` By using the above code, we can simply show the word cloud of the most common words in the Reviews column in the dataset. So, Finally, we have done all concepts with theory and implementation of NLP in Python…..\! ## **Advantages of NLP** - Removes unnecessary information. - NLP helps computers to interact with humans in their languages ## **Disadvantages of NLP** - NLP may not show full context. - NLP is unpredictable sometimes. ## **Everyday NLP examples** There are many common day-to-day life applications of NLP. Apart from virtual assistants like Alexa or Siri, here are a few more examples you can see. - Email filtering. Spam messages whose content is malicious get automatically filtered by the Gmail system and put into the spam folder. - Autocorrection of any text by using techniques of NLP. Sometimes we see that in mobile chat application or google search our word/sentence get automatically autocorrected. This is because of NLP. - Text classification of tweets or reviews whether they are talking positively or negatively in the text. ## **Conclusion** In this tutorial for beginners we understood that NLP, or Natural Language Processing, enables computers to understand human languages through algorithms like sentiment analysis and document classification. Using NLP, fundamental deep learning architectures like transformers power advanced language models such as ChatGPT. Therefore, proficiency in NLP is crucial for innovation and customer understanding, addressing challenges like lexical and syntactic ambiguity. Python programming language, often used for NLP tasks, includes NLP techniques like preprocessing text with libraries like NLTK for data cleaning. Given the power of NLP, it is used in various applications like text summarization, open source language models, text retrieval in search engines, etc. demonstrating its pervasive impact in modern technology. #### Key Takeaways - NLP (Natural Language Processing) revolutionizes human-computer interaction, enabling machines to understand and interpret human languages effectively. - NLP encompasses Natural Language Understanding (NLU) and Generation (NLG), addressing challenges like lexical and syntactic ambiguity for accurate interpretation and generation of text. - Python serves as a fundamental tool for NLP implementation, offering libraries like NLTK for text preprocessing and data cleaning. - NLP finds extensive real-world applications including email filtering, autocorrection, and text classification, driving innovation and automation across industries. ***The media shown in this article on Natural Language Processing are not owned by Analytics Vidhya and is used at the Author’s discretion.*** [](https://www.analyticsvidhya.com/blog/author/amruta99/) I am Software Engineer, data enthusiast , passionate about data and its potential to drive insights, solve problems and also seeking to learn more about machine learning, artificial intelligence fields.