This post focuses on NLP and its increasing use in what has come to be known as NLP sentiment analytics.
By now, it is evident that humans and machines do not speak the same language. It is also pretty clear that the two also do not share “emotions”, thoughts, or feelings.
However, in a world now witnessing the 4.0 version of the industrial revolution, with new technologies being born or commercially deployed almost daily, there’s an urgency for humans and machines to be on the same page.
Helping in this task are technologies like artificial intelligence (AI), machine learning (ML), deep learning, cognitive computing, and Natural Language Processing (NLP).
What is NLP Sentiment Analysis?
With the advent of digital technology, the Internet, and the World Wide Web, enterprises today are inundated not only with copious but also a wide variety of data. Off the cuff, brands have to deal with customer calls, emails, social media posts, polls, and so on.
Obviously, enterprises need to make sense of it all, which requires a great deal of time, energy, and effort.
One way to achieve this is to deploy NLP to extract information from text data, which can then be used in computations.
Sentiment analytics is emerging as a critical input in running a successful business. Want to know more about Express Analytics' sentiment analysis service? Speak to Our Experts to get a lowdown on how Sentiment Analytics can help your business.
So, very quickly, NLP is a sub-discipline of AI that enables machines to understand and interpret human language. It’s one of the ways to bridge the communication gap between man and machine.
Want actionable insights that increase customer experience and conversions? ---> Let's Talk
Where is NLP going? Where is it used?
So far, NLP has been primarily used in text analytics. With NLP, this form of analytics categorizes words into a predefined structure before extracting meaning from the text content.
NLP is used to derive changeable inputs from the raw text for either visualization or as feedback to predictive models or other statistical methods.
However, with the advent of new technology, analytics vendors now offer NLP as part of their business intelligence (BI) tools.
For example, from the text, NLP sentiment analysis is now used to make sense of “voice” in interfaces such as digital voice assistants or smart speakers, like Amazon’s Alexa, as they become increasingly interactive.
NLP Sentiment Analytics
To get a relevant result, everything needs to be put in a context or perspective. When a human uses a string of commands to search on a smart speaker, for the AI running the smart speaker, it is not sufficient to “understand” the words.
It also needs to provide context to the spoken words used and attempt to understand the searcher’s ultimate goal behind the search.
What keeps happening in enterprises is the constant influx of vast amounts of unstructured data generated from various channels – from interactions with customers or leads to social media reactions, and so on.
Now, to make sense of all this unstructured data, you require NLP, as it gives computers the ability to read and derive meaning from human languages.
A major differentiator, if we can call it that, between text analytics and sentiment analysis is that in the former, NLP is used to analyze copious amounts of text (unstructured data), examine the grammar used, identify patterns in it, and draw conclusions.
Sentiment analysis goes beyond that – it tries to figure out if an expression used, verbally or in text, is positive or negative, and so on.
Connecting both is NLP, though. To put it in another way, text analytics is about “on the face of it”, while sentiment analysis goes beyond, and gets into the emotional terrain.
E.g., Why did a user use a certain emoticon to describe a product on social media? Your chatbots can also be integrated into sentiment analytics.
Emotions give you away, they say. But so far, it was only possible for a fellow human being to recognize those emotions.
Now, there’s a need for machines to understand them, find patterns in the data, and provide feedback to the analysts.
Sentiments have become a significant value input in the world of data analytics. Therefore, NLP for sentiment analysis focuses on emotions, enabling companies to understand their customers better and improve their overall experience.
Because emotions give a lot of input around a customer’s choice, companies give paramount priority to emotions as the most important value of the opinions users express through social media.
Therefore, NLP for sentiment analysis focuses on emotions and uncovers situations that help companies better understand their customers, thereby improving the customer experience, which in turn enables businesses to change their market position.
An example of a successful implementation of NLP sentiment analytics (analysis) is the IBM Watson Tone Analyzer. It understands emotions and communication style, and can even detect fear, sadness, and anger in text.
NLP Sentiment Analysis: Transforming Finance & Banking Industry
Listed below are the applications of sentiment analysis in the finance services sector:
Study of audience emotional responses
Financial organizations utilize AI-based tools to process and analyze vast amounts of data, identifying various sentiments associated with customer conversations about banks in their social media posts, comments, reviews, or survey form submissions.
More powerful credit market tracking
Sentiment analysis can be used by financial institutions to monitor credit sentiment from the media.
Remarkable NLP tools can process such data from a sentimental perspective.
Links between the performance of credit securities and media updates can be identified by AI analytics.
Compliance tracking in banks
NLP-enabled sentiment analysis can produce various benefits in the compliance-tracking region.
Compliance departments in banking domains and other financial organizations have an abundance of records related to compliance rules, similar to financial trading data, and they must routinely update their procedures to comply with these requirements.
AI-based sentiment analysis systems are used to enhance the procedure by processing vast amounts of data and classifying each update based on relevance.
Want actionable insights that increase customer experience and conversions? ---> Let's Talk