We have always heard “This call may be recorded for quality and training purposes” when we call the company’s call centres for required services. Although some calls are used for training purposes, but these are even used to improve natural language processing algorithms. From onsite customer behaviour to daily or seasonal trends, the typical data warehouse can contain a diverse blend of data. The insights gained from this information have driven businesses into a new domain of customer understanding, but limiting the analytics to this type of highly structured format excludes the majority of the data that’s being created at present. 80% of the data created is unstructured. It’s generated from conversations with customer service representatives and on social media sites, as well as other places. Organisations are turning to Natural Language Processing (NLP) technology to derive understanding from the countless unstructured data available online and in call logs.
In short, Natural Language Processing gives machines the ability to read, understand and derive meaning from the human languages. The challenge here with Natural Language Processing is that computers normally requires humans to talk in the programming language, which has to be explicit and highly structured, although natural language is anything but explicit. Due to highly structured languages, it’s always been difficult for machines to grasp the context of human language. But with the help of Machine Learning computers determine the uncertainty of human language.
Sentiment analysis is widely used in the web and social media monitoring as it allows businesses to gain a broad public opinion on the organization and its services. The ability to extract insights from the text and emoticons from social media is a practice that is widely adopted by the organizations worldwide. The capacity to hastily understand customer’s attitudes and responses accordingly is something that companies like Expedia took advantage of. Digital media represents an enormous opportunity for businesses of any industry to acquire the needs, opinions and intent that users share on the web and social media. Listening to consumer’s voice requires a deep understanding of what customer’s express in Natural Language: NLP is the best way to understand the human language and crack the sentiment behind it.
Although companies always consider sentiments (positive or negative) as the most significant value of the opinions users express through social media, the reality is that emotions provide a lot of information that addresses customer’s choices and it even determines their decisions. Due to this, NLP for sentiment analysis focused on emotions reveals itself extremely favourable. With the help of NLP, companies can understand their customers better to improve their experience, which will help the businesses change their market position.
For example: If customer complaints through message or email about their issues with service or product, a Natural Language Processing system would recognize the emotions, analyze the text and mark it for a quick automatic reply accordingly. All this can save company’s time and money too. Or even companies can search for mentions on the web and social media about their Brands and quantify whether the context was negative, neutral or positive.
Email filters are one of the common use cases of Natural Language Processing. By analyzing the text in the emails that flow through the servers, email providers can stop spam based email contents from entering their mailbox.
Email Filtering to avoid Spam emails
There are tools developed with the help of Natural Language Processing that enable companies to create intelligent voice driven interfaces for any system. Businesses are employing Natural Language Processing technologies to understand human language and queries. Instead of trying to understand concepts based on normal human language usage patterns, the company’s platform depends on a custom knowledge graph that is created for each application and perform a much better job identifying concepts that are relevant in the customer domain.
Many important decisions in businesses are progressively moving away from human oversight and control. Many of the business decisions in industries like Finance are driven by sentiments influenced by the news. The majority of the news content is present in the form of text, infographics and images. A considerable task, of Natural Language Processing, is taking these text, analyze and extract the related information in a format that can be used in decision-making capabilities. For example, news of a big merger can impact business decisions and integrated into trading algorithms which can have profit implications in the millions of dollars.
With the arrival of advanced statistical algorithms, programs are now capable of using statistical inference to understand the human conversation by calculating the probability of certain results. The program incorporating Natural Language processing and Machine Learning can constantly improve itself with more data it processes. All the insights hidden in the unstructured data are becoming more feasible with technology advancement. Natural Language Processing is gaining huge traction and enormous potential for the businesses.
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