One of the most revolutionary uses of computer machines is Artificial Intelligence (AI) which performs various human-like tasks and simulates human intelligence. One such AI technology is Natural Language Processing (NLP), which helps understand, analyze and extract the essence of words spoken or written by human beings. 

As a student of Computer Science or an IT professional who wants to pursue a career in AI, perhaps you would like to consider learning AI technologies like NLP and Text Mining. So why don’t you check out this Natural Language Processing and Text Mining Free course to learn about the topic and how to leverage these in your workplace? 

What is Natural Language Processing (NLP)

NLP is a field that intersects linguistics and computation. As the term suggests, it relates to natural language and linguistics to understand and analyze speech and sentiments. The computer program processes spoken and written language like the human brain does. Similar to how the various senses of a human being input and process spoken words and images to make sense, the computer software computes text and speech into meaningful insight by converting the natural language into a code the computer can understand.

This ability of the computer software to analyze text is called “natural language” “processing.”

Where is it used

NLP is used in many areas like text summarization, text mining, text classification, machine translation, relationship extraction, entity recognition, automated question answering, natural language generation using algorithms, and many more.

Applications include:

  • Chatbots
  • Social Media (sentiment analysis, trending topics, popular hashtags, etc.)
  • Search Engine Results (topics extraction from news feeds, automated translation, content extraction, etc.)
  • Cyber Security (monitoring malicious attacks, phishing, fraudulent activities, etc.)
  • Customer Satisfaction (feedback analysis, customer service automation, etc.)
  • Content Automation for various client needs (news extraction and analysis, plagiarism and grammar check, etc.)
  • Healthcare (content analysis and categorization of medical records for disease analysis and prevention, healthcare management, insurance policies, etc.)
  • Human Resource (HR) Management (talent hunt and hiring based on keywords and phrases)
  • Stock Forecasting and Trading (analyzing market history, extracting trade patterns, summarization of financial performance, etc.)

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What is Text Mining

Text Mining is a computer program that processes, analyzes, and extracts information from text. Also known as Text Analytics in some use cases, it involves the automated transformation of text into a structured format for identifying concepts, patterns, and meaningful insights.

It is an AI technology that leverages NLP to transform unstructured and semi-structured text from websites, databases, blogs, social media, feeds, documents, blogs, etc., to normalized structured data for analysis and Machine Learning algorithms.

It identifies relationships, patterns, facts, and information buried in the massive Big Data.

The transformed data presented as

a) clustered HTML tables, charts, mind maps, etc., 

b) analyzed directly,

c) integrated into BI dashboards for data intelligence,

d) integrated into databases for integrated analysis, or

e) preprocessing techniques like tokenization, stemming, and lemmatization.

Applications of Text Mining

Text mining is generally used for historical as well as streaming data.

Applications include

  • Text Categorization (e.g., email spam identification)
  • Document Classification (news feed categorization as local/national/international, sports, lifestyle, and so on)
  • Document Summarization (news analysis, market trend spotting)
  • Sentiment Analysis (sentiments on new product rollout from the Internet and social media)
  • Entity Extraction/Identification/Recognition (where Machine Learning algorithms identify mentions of certain entities from large text, customer support, search engines, and so on)
  • Some real-world use cases are risk management, business intelligence, content enrichment, cybercrime prevention, contextual advertising, customer service, knowledge management, call center, the Internet, social media, fraud detection, and marketing management. 

A Primer on NLP Techniques

NLP has gained traction because of its ability to understand human language and leverage it to make lives easier. While the interest ramped up after chatbots and machine translation, various applications have generated new products like Alexa and Siri.

There are various techniques used in NLP. Each one is the best fit for a particular scenario. Some commonly applied techniques are:

Sentiment Analysis

Sentiment Analysis is an application of Machine Learning techniques and uses both supervised and unsupervised learning.

In the era of social media, blogs, and interactive comment pages, Sentiment Analysis has gained popularity. Whether to understand consumer sentiments after a change in the UI of a service or customer satisfaction after a new product design, it is critical for customer sentiment analysis and marketing plans.

Social Media is a part of everyday lives, where users tweet, retweet, like, and comment on various issues, from political matters to the new Amazon Prime design and movie reviews. Sentiments are extracted from the text and extrapolated simply as negative, positive, or neutral sentiments. Common uses are the identification of hate speech in social media and distressed customers with negative views and reviews.

Keyword Extraction

Keyword Extraction is one of the more simplistic NLP techniques, involving the extraction of words and expressions in most frequent use and further summarising it for the presentation of the results.

The algorithms extract phrases and words, whether general text or colloquialism. The technique finds application in social media monitoring, analysis of customer feedback, and search engine optimization.

Topic Modeling

Keyword Extraction techniques use algorithms to condense the text to main keywords and hidden themes and generate topics. The method uses unsupervised machine learning, so documents do not require labeling.

Summarization

Text Summarization condenses a large amount of text into a small chunk.  

The technique summarizes long news articles and research papers and generates condensed content for search engine results. It works together with other NLP techniques of Topic Modeling and Keyword Extraction. 

A two-step process: extract and abstract, is part of the Summarization technique. In the ‘extract’ step, algorithms extract a summary based on the frequency of key sections of the text. In ‘abstract,’ the algorithms produce a new summarized text for higher ranking.

Named Entity Recognition (NER)

NER is an NLP technique used to extract entities from a body of text and identify concepts such as dates, places, names of people or countries, etc. The process identifies the entity and then categorizes the same. Application of linguistics and pre-training of the model are the key considerations.

NER is applied in building recommendation systems and in academia.

Stemming and Lemmatization

Stemming algorithms consider suffixes and prefixes to work sequentially and derive the word root.

Although Lemmatization and Stemming are advanced techniques, Lemmatization essentially removes the limitations of Stemming to extract the correct lemma of words. Knowledge of linguistics and grammar is essential for training the algorithms for minimal noise.

Stop Words Removal

The preprocessing step after Stemming and Lemmatization is Stop Words Removal. The technique considers words irrelevant to the main message or content, such as prepositions and conjunctions. Although such words are in frequent use, they do not contribute to the meaning.

So Stop Words Removal takes these words and cleans them up before modeling, using Python libraries such as SpaCy and Gensim. The unnecessary weightage of these words is removed for more efficient modeling.

TF-IDF

It is a Statistical technique to measure the importance of a word in a collection of documents by calculating how frequently a word appears.

Summary

Although the above two technologies differ, they are both parts of the AI ecosystem and offer business advantages from analysis and resource optimization. However, in some use cases, one is better than the other, and the IT professional who knows to apply NLP or Text Mining in his work can help optimize his business and support the rollout of new products or services.