Natural language processing uses tools, techniques, and algorithms to make meaning of human language. Human language data includes text, speech, images, videos, and so on. So, we will see how to get meaningful data from the entire data using natural language processing. Here is a practitioner’s guide to natural language processing.
Natural language processing is an exclusive field relating to computer science and artificial intelligence. Also, it is used to design systems that can understand human language. So, the start of leaning natural language processing may seem difficult. However, you need to be motivated and focussed. Once you get the hang of it, you will be able to solve problems using natural language processing.
Practitioner’s Guide: Standard NLP Workflow
For natural language processing, we will be using the industry-standard CRISP-DM model. The workflow starts with the text document. We then pre-process the text and do exploratory data analysis to get primary data. Then we do text representation and feature engineering of the data. Based on the problem, we model a supervised model or unsupervised model.
Scraping News Article For Data Retrieval
In this stage of natural language processing, we use the request and BeautifulSoup libraries. Hence, the request library is used to access HTML content. BeautifulSoup is used to extract data from the site. So, using these libraries, we can extract useful data. Now, scraping is the first step in natural language processing.
Practitioner’s Guide: Text Wrangling & Pre-Processing
In this step of natural language, we will clean and pre-process the data. Also, we use nltk and spacy libraries in this process.
Practitioner’s Guide to Natural Language Processing: Removing HTML Tags
Web and screen scraping will give us a lot of redundant data. Hence, we need to remove unnecessary Html tags to get relevant data.
Removing Accented Characters
The text data will also include accented characters. Natural language processing uses ASCII characters. Therefore, we need to convert accented characters to ASCII characters.
Shortened words are called contractions. Hence, we need to develop it so that we can use it in the natural language processing process. We use functions to expand contraction.
Removing Special Characters
In natural language processing, we cannot use special characters and symbols. Hence, sometimes even numeric characters need to be removed.
Stemming in natural language processing is to get the base word of larger words. Also, it helps us to get essential words by using only the base form. It is used to cluster text. However, we use the Porter stemmer library in natural language processing.
Practitioner’s Guide to Natural Language Processing: Lemmatization
Lemmatization is similar to stemming; the only difference being the base work will be a word from the dictionary.
Removing Stop words
In natural language processing, we need to remove stop-words like a, an, and, the. Moreover, these words and meaningless and has no value.
Bringing It All Together – Building A Text Normalizer
Once we clean the data, we need to pre-process the data. In natural language processing, we build a text normalizer. Now we are done with the first two steps of natural language processing.
Understanding Language Syntax And Structure
Understanding the syntax and structure is the first step. Also, we convert phases to clauses and clauses into sentences. Understanding the syntax and structure is the most crucial part of natural language processing. So, we will see the four different parsing techniques.
1. Parts Of speech Tag
In this, we categorize words based on context. The categories are:
- Noun – The POS tag is N
- Verb – The POS tag is V
- Adjective – The POS tag is ADJ
- Adverb – The POS tag is ADV
Other sub-categories are a singular noun (NN), singular proper noun (NNP), and plural noun (NNS)
2. Shallow Parsing Or Chunking
Shallow parsing is an important natural language processing technique. In shallow parsing, we use phrases and not just words.
Practitioner’s Guide to Natural Language Processing: 3. Constituency Parsing
This natural language processing technique is used to understand the constituents of the sentence. The phrases use specific rules called phrase structure rules.
4. Dependency Parsing
In this technique, we analyze the structure as well as the semantics of the sentences. The principle is that all words have some connections with other words in the sentence.
Practitioner’s Guide to Natural Language Processing: Named Entity Recognition
Some words have more information and are unique in some ways. Now, these words are known as named entities. However, named entity recognition is an essential technique of natural language processing.
Emotion And Sentiment Analysis
It recognizes the context of the sentences and quantifies the sentiment as negative, positive, or neutral. So, the two major approaches to the sentiment analysis are:
- Deep learning approach
- Unsupervised lexicon-based approach
The popular lexicons used are:
- AFINN lexicon
- Bing Liu’s lexicon
- MPQA subjective lexicon
- VADER lexicon
- TextBlob lexicon
We have seen the different tools and techniques used for natural language processing. So, you should be able to work with a text document and start analyzing the data. Keep coding and making life easy.