Innovative applications of natural language processing in business communication are helping companies monitor customer interactions, gain critical insights from unstructured data sets and ensure effective communications. NLP tools like text prediction and autocorrect are used by people to quickly write documents without making grammar errors.
NLP (Natural Language Processing) is an interdisciplinary field combining computational linguistics (rule-based modeling of human language) with statistical, machine learning and deep learning models. Discover some of its most prominent and practical applications within business!
1. Text Analytics
Unstructured text sources like customer support tickets, surveys and social conversations contain vast amounts of data that can be harvested in order to gain meaningful insights for businesses. If this data is properly analysed it could provide them with powerful new avenues.
These insights can be leveraged to improve business processes, products, or services. HR professionals may utilize text analytics to examine employee reviews in order to detect potential sources of high agent turnover rates before it’s too late; or find patterns in employee sentiment that affect productivity or customer experience.
There are various approaches to text analysis available to businesses today; choosing one depends on your analysis goals. Common strategies include information retrieval, lexical analysis of word frequency distributions, pattern recognition and tagging/annotation as well as link/association analysis techniques. Text analytics may also be used for text classification – assigning predefined tags based on semantic meaning to texts which can help identify common phrases within documents as well as classify documents by topic.
2. Text Mining
Text analytics is a subset of natural language processing that uses automated analysis to detect patterns and trends within unstructured data such as documents, images, emails, social media messages, customer evaluations or any other content that exists outside a structured format such as databases. Businesses might employ text analytics to detect signs of diminishing consumer satisfaction early on and take appropriate actions before any issues affect sales or brand reputation negatively.
Businesses generate enormous volumes of information every day, from social media posts and public news stories to internal emails and IoT data. Much of this textual data needs advanced tools to understand, which is why more organizations are turning to text mining to unlock insights hidden in their data.
To gain information from documents, natural language processing techniques like tokenization, stemming and stop word removal may be needed to extract specific details. Next, text analysis tools and machine learning algorithms are often utilized to examine text data for keywords, phrases and patterns that can be further examined for analysis purposes. Often this type of text processing can be enhanced through using ontologies/vocabularies that offer sets of terms with synonyms organized hierarchically (such as Tylenol/acetaminophen).
3. Natural Language Understanding
Natural Language Understanding (NLU) is an artificial intelligence technology used to interpret human speech. NLU analyses mispronunciations, grammatical errors, and misspelled words to ascertain someone’s true intent – helping chatbots and AI assistants respond more empathetically when responding to users.
NLU technology is also essential to business applications such as text summarization and customer support chatbots, providing companies with the means to understand customer queries more quickly and route them directly to the relevant department, improving both service delivery and productivity overall.
NLU (Natural Language Understanding) is one subtopic of Machine Learning (ML), the practice of turning raw data into actionable insights using algorithms. NLP plays a central role in this process by taking free-form text and structuring it according to certain standards; various algorithms then operate on that text for identification sentiment analysis, entity recognition and semantic understanding. NLU often pairs up with Natural Language Generation which creates human-sounding text from raw data inputs such as summarization or description or converts it into speech for use with assistants or chatbots.
4. Natural Language Generation
Natural Language Generation takes text analytics and sentiment analysis one step further by producing text that is readable, accurate, grammatically correct, in the style of user’s chosen language, as well as being accessible and legible for future readers. You might see this technology at work in sports reporting, financial data updates or virtual assistants like Siri or Alexa.
NLG systems use natural language processing (NLP) technologies to identify keywords, phrases and sentiments within unstructured text that help you better understand customer needs. Relevant sentences or sections thereof are then combined, rewritten using grammar rules and created into formats chosen by either user or programmer.
NLG allows your team to use data analysis results more effectively by customizing information for individual customers based on them. This reduces manual reporting time, increasing analytic productivity. If, for example, you want to communicate findings on COPD trends and statistics quickly and efficiently using NLG reports or emails, NLG can summarize that data for you with personalized responses such as transcribing voice commands into customized customer-directed text responses.
5. Automated Text Analysis
Automated Text Analysis refers to the application of machine learning technology on data that has been semi-manually entered into systems or software, such as customer support tickets, instant messages (IMs), social media posts, surveys or feedback forms as well as product reviews in an e-commerce system.
Although not as involved as Natural Language Understanding or Text Mining, Data Visualization still can provide a good level of insight into your data. It allows you to quickly organize and analyze large volumes of unstructured information quickly – something especially helpful for quickly identifying urgent issues to be addressed, uncovering sentiment about products/services offered or uncovering customer satisfaction/loyalty trends.
Note that automated text analysis accuracy varies based on the amount and complexity of data processed. Optimizing recall vs precision can be tricky; an ideal text analysis platform should offer multiple visualization tools to help understand results; these should provide flexibility and can be personalized according to individual needs.
