If data is King, then context is Queen and a sentiment analysis API holds the keys to the kingdom. During this article we’ll walk through the basics of sentiment analysis, the role of natural language processing, and how sentiment analysis APIs are used by companies to inform strategic decision making.
Before answering the above question, let’s first clear up what is meant by a sentiment analysis.
Sentiment analysis is a research area examining people’s opinions, attitudes and emotions in written language through the method of Natural Language Processing (NLP). The Oxford Dictionary explains this as:
The process of computationally identifying and categorising opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Natural Language Processing (NLP) technology powers sentiment analysis by mining text with the aim of extracting subjective information from the data. Most sentiment analysis providers lean on advanced linguistic algorithms and machine learning to deliver granular sentiment detection and understand and derive meaning from human language. This helps sentiment analysis go beyond categorising data at face value, for example, tricky sentences like double negatives (e.g. not bad) and irony can be correctly categorised based on positive, negative and neutral tonality.
APIs enable businesses to blend data from their own applications with third-party business systems. Through the integration of data, executives can yield a representative picture of their business and the eco-system it sits within, both on a macro and micro level. But data needs context. Without context, professionals run the risk of looking at data from a vanity perspective. Here’s where sentiment analysis APIs help. Sentiment analysis prevents companies from looking at data with rose tinted glasses, and APIs ensure they gain a full 360-view of their business.
The befits of sentiment analysis APIs are vast, including helping organizations:
Let’s take a look at the benefits sentiment analysis APIs have on critical business decision making in more depth!
Don’t underestimate the importance of blending datatypes. It’s vital that data and teams don’t exist in silos. Lack of visibility can be hugely detrimental to businesses, for example, while your sales figures may be looking positive, if your customer contact centre sees a spike in negative language, the future may not look at bright as first expected. Perhaps new customers have been wrongly sold to or there’s a fault with your product, such insights are relevant to numerous departments.
Regardless of whether it’s a sentiment analysis API or standard API, APIs ensure you're fully aware of your surroundings by connecting the dots between disparate data types that usually sit siloed in different departments. More often than not, APIs are fed into command centres that visualize all business functions from one centralised platform. While data analysis looks different from company to company, one thing most businesses have in common is the need for accessing a unified reporting solution that has the ability to show one source of truth – a single and unified view of all business functions.
Sentiment analysis APIs are usually favoured as they tell executives the why (qualitative insights) and not just the what (quantitative insights), therefore adding critical context and guiding their decision-making process.
Hopefully by now you have a better understanding of the role of sentiment analysis APIs and how they’re used to inform business strategies. Want to see what this looks like for your company? Fill out the form below and we’ll show you!