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What is natural language processing? The ultimate guide

Hey Alexa, what’s natural language processing?

Natural language processing (NLP) is a branch of Artificial Intelligence which aims to comprehend written or spoken words. The goal is to create a system that can interpret language in the same way as humans do.

Most of us use NLP on a daily basis – from Amazon Alexa to iPhone autocorrect and Google Translate – all of these everyday tools utilize complex technology and algorithms to make your life easier. 

How does natural language processing work?

Natural language processing is rooted in computational linguistics and utilizes either machine learning systems or rule-based systems. These areas of study allow NLP to interpret linguistic data in a way that accounts for human sentiment and objective. 

Computational Linguistics:

  • Aptly named, computational linguistics is the intersection of linguistics and computer science to analyze and manufacture language.

Rule-Based System:

  • Rule-based systems work under a predetermined set of linguistic rules created by a human. 

Machine Learning:

  • Machine learning is the use of algorithms to create computer systems with the ability to learn autonomously by recognizing statistical patterns. 

Deep Learning:

  • Within the machine learning family, deep learning attempts to mimic the human brain using layered algorithms called artificial neural networks, which result in advanced analysis. 

These systems enable a computer to analyze, understand and interpret meaning from written or spoken natural language. NLP technologies streamline processes by organizing and structuring textual data to complete a range of tasks.

What can natural language processing do?

Sentiment analysis

  • Sentiment analysis is the practice of using computers to identify and categorize opinions and emotions expressed in text. This form of analysis reveals the writer’s sentiment as demonstrated by word choice. Natural language processing recognizes subtle nuances in language to classify the opinion as positive, negative or neutral.

Text classification

  • Text classification streamlines manual analysis to understand and classify unstructured text. This tool can be used to autonomously categorize text data like open-ended survey responses by written attributes.

Chatbots and virtual assistants

  • These technologies utilize natural language processing to communicate with humans by understanding language use and responding appropriately. Machine learning systems adapt over time, using previous interactions to inform current and future queries. 

Information extraction

  • Information extraction sifts through text to recognize bits of desired data or keywords. This practice can be used to extract small pieces of information like a name or address from a larger piece of language data. Keyword extraction demonstrates the frequency of certain words or phrases to reveal trends and patterns.

Translation

  • Machine translation eliminates language boundaries, allowing us to communicate more effectively. Natural language processing has been the driving force in the progression of language translation technologies. Using machine learning and large amounts of data, tools like Google Translate are able to autonomously improve.

Summarization

  • Text summary tools use NLP to read over larger text files and recognize important information. Technologies typically either extract specific keywords or paraphrase the original text with relevant findings.  

Autocorrect

  • NLP doesn’t just recognize words, it analyzes grammar. Autocorrect tools detect errors in grammar and provide revisions and suggestions to improve readability.

Speech recognition

  • Speech recognition uses natural language processing to receive and convert spoken language into a format readable by a computer. Virtual assistants, speech to text tools, voicemail transcription, audio translation technologies and more all utilize NLP.

The components of NLP

Natural language understanding

Natural language understanding utilizes syntactic and semantic analysis to interpret textual meaning. By understanding grammatical structure and textual context, a computer can comprehend and derive meaning from natural language. NLU utilizes techniques like:

  • Word sense disambiguation: Word sense disambiguation is the process of interpreting the meaning of a word within the context of the larger text. Words like crane have dual meanings. Are we talking about a bird or heavy machinery? 

Natural language generation

Natural language generation systems transform data into human-like sentences and phrases based on NLU’s interpretation of a text. Based on data input, a computer can generate an accurate natural language response or paraphrase through summarization. NLU and NLG work hand in hand to read, analyze and respond to natural language.

How can I utilize natural language processing?

Relative Insight harnesses the power of natural language processing to extract meaningful insight from any textual data source. Our technology compares two or more written data sets to unearth the differences in topics, words, phrases, grammar and emotion. This method provides an additional layer of context, allowing you to weed out common language to discover what makes each text unique. We can level up:

Competitor benchmarking 

Our natural language processing technology enhances your contextual understanding of the marketplace. Competitor benchmarking analysis examines your brand in comparison to direct competitors or a wider industry. Relative Insight discovers the strengths and weaknesses of your brand to help you rise above the competition.

Case Study: Analyzing social media output from five burger brands

Topic or brand analysis 

Our NLP technology takes a new approach to topic and brand analysis. Relative Insight utilizes the power of comparison to analyze different demographics, locations, how language has changed over time and more.

Case Study: Comparing NFL fan conversations ins the UK and US

Measuring campaign effectiveness 

Natural language processing allows you to measure the qualitative impact of your brand’s marketing efforts. Go beyond quantitative metrics and compare conversation before and after a campaign or newsworthy event to uncover public response. 

Case Study: How Elon Musk single-handedly impacted the Bitcoin market

Survey analysis

Save time and energy with automated and effective survey analysis. Relative Insight does the work by analyzing open-ended survey responses and comparing respondents by a range of metadata points. Discover trends and patterns develop unique tone of voice unique to target audiences.

Case study: Analyzing survey data about periods

Voice of the customer

Speak the language of your audience by uncovering the unique voice of the customer for your brand. Relative Insight analyzes social media conversations, forum posts, customer reviews and open ended survey responses to discover how your consumers speak. This insight can drive marketing communications ensuring your messaging resonates.

Case study: How do rival brands develop unique tone of voice: Coke vs. Pepsi

Discover NLP