October 2023

What is Sentiment Analysis? – Ultimate Guide

Posted on October 6, 2023August 15, 2025 The Quiq Team

A person only reaches out to a contact center when they’re having an issue. They can’t get a product to work the way they need it to, for example, or they’ve been locked out of their account.

The chances are high that they’re frustrated, angry, or otherwise in an emotionally-fraught state, and this is something contact center agents must understand and contend with.

The term “sentiment analysis” refers to the field of machine learning which focuses on developing algorithmic ways of detecting emotions in natural-language text, such as the messages exchanged between a customer and a contact center agent.

Making it easier to detect, classify, and prioritize messages on the basis of their sentiment is just one of many ways that technology is revolutionizing contact centers, and it’s the subject we’ll be addressing today.

Let’s get started!

What is Sentiment Analysis?

Sentiment analysis involves using various approaches to natural language processing to identify the overall “sentiment” of a piece of text.

Take these three examples:

“This restaurant is amazing. The wait staff were friendly, the food was top-notch, and we had a magnificent view of the famous New York skyline. Highly recommended.”
“Root canals are never fun, but it certainly doesn’t help when you have to deal with a dentist as unprofessional and rude as Dr. Thomas.”
“Toronto’s forecast for today is a high of 75 and a low of 61 degrees.”

Humans excel at detecting emotions, and it’s probably not hard for you to see that the first example is positive, the second is negative, and the third is neutral (depending on how you like your weather.)

There’s a greater challenge, however, in getting machines to make accurate classifications of this kind of data. How exactly that’s accomplished is the subject of the next section, but before we get to that, let’s talk about a few flavors of sentiment analysis.

What Types of Sentiment Analysis Are There?

It’s worth understanding the different approaches to sentiment analysis if you’re considering using it in your contact center.

Above, we provided an example of positive, negative, and neutral text. What we’re doing there is detecting the polarity of the text, and as you may have guessed, it’s possible to make much more fine-grained delineations of textual data.

Rather than simply detecting whether text is positive or negative, for example, we might instead use these categories: very positive, positive, neutral, negative, and very negative.

This would give us a better understanding of the message we’re looking at, and how it should be handled.

Instead of classifying text by its polarity, we might also use sentiment analysis to detect the emotions being communicated – rather than classifying a sentence as being “positive” or “negative”, in other words, we’d identify emotions like “anger” or “joy” contained in our textual data.

This is called “emotion detection” (appropriately enough), and it can be handled with long short-term memory (LSTM) or convolutional neural network (CNN) models.

Another, more granular approach to sentiment analysis is known as aspect-based sentiment analysis. It involves two basic steps: identifying “aspects” of a piece of text, then identifying the sentiment attached to each aspect.

Take the sentence “I love the zoo, but I hate the lines and the monkeys make fun of me.” It’s hard to assign an overall sentiment to the sentence – it’s generally positive, but there’s kind of a lot going on.

If we break out the “zoo”, “lines”, and “monkeys” aspects, however, we can see that there’s the positive sentiment attached to the zoo, and negative sentiment attached to the lines and the abusive monkeys.

Why is Sentiment Analysis Important?

It’s easy to see how aspect-based sentiment analysis would inform marketing efforts. With a good enough model, you’d be able to see precisely which parts of your offering your clients appreciate, and which parts they don’t. This would give you valuable information in crafting a strategy going forward.

This is true of sentiment analysis more broadly, and of emotion detection too.
You need to know what people are thinking, saying, and feeling about you and your company if you’re going to meet their needs well enough to make a profit.

Once upon a time, the only way to get these data was with focus groups and surveys. Those are still utilized, of course. But in the social media era, people are also not shy about sharing their opinions online, in forums, and similar outlets.

These oceans of words from an invaluable resource if you know how to mine them. When done correctly, sentiment analysis offers just the right set of tools for doing this at scale.

Challenges with Sentiment Analysis

Sentiment analysis confers many advantages, but it is not without its challenges. Most of these issues boil down to handling subtleties or ambiguities in language.

Consider a sentence like “This is a remarkable product, but still not worth it at that price.” Calling a product “remarkable” is a glowing endorsement, tempered somewhat by the claim that its price is set too high. Most basic sentiment classifiers would probably call this “positive”, but as you can see, there are important nuances.

Another issue is sarcasm.

Suppose we showed you a sentence like “This movie was just great, I loved spending three hours of my Sunday afternoon following a story that could’ve been told in twenty minutes.”

A sentiment analysis algorithm is likely going to pick up on “great” and “loved” when calling this sentence positive.

But, as humans, we know that these are backhanded compliments meant to communicate precisely the opposite message.

Machine-learning systems will also tend to struggle with idioms that we all find easy to parse, such as “Setting up my home security system was a piece of cake.” This is positive because “piece of cake” means something like “couldn’t have been easier”, but an algorithm may or may not pick up on that.

Finally, we’ll mention the fact that much of the text in product reviews will contain useful information that doesn’t fit easily into a “sentiment” bucket. Take a sentence like “The new iPhone is smaller than the new Android.” This is just a bare statement of physical facts, and whether it counts as positive or negative depends a lot on what a given customer is looking for.

There are various ways of trying to ameliorate these issues, most of which are outside the scope of this article. For now, we’ll just note that sentiment analysis needs to be approached carefully if you want to glean an accurate picture of how people feel about your offering from their textual reviews. So long as you’re diligent about inspecting the data you show the system and are cautious in how you interpret the results, you’ll probably be fine.

How Does Sentiment Analysis Work?

Now that we’ve laid out a definition of sentiment analysis, talked through a few examples, and made it clear why it’s so important, let’s discuss the nuts and bolts of how it works.

Sentiment analysis begins where all data science and machine learning projects begin: with data. Because sentiment analysis is based on textual data, you’ll need to utilize various techniques for preprocessing NLP data. Specifically, you’ll need to:

Tokenize the data by breaking sentences up into individual units an algorithm can process;
Use either stemming or lemmatization to turn words into their root form, i.e. by turning “ran” into “run”;
Filter out stop words like “the” or “as”, because they don’t add much to the text data.

Once that’s done, there are two basic approaches to sentiment analysis. The first is known as “rule-based” analysis. It involves taking your preprocessed textual data and comparing it against a pre-defined lexicon of words that have been tagged for sentiment.

If the word “happy” appears in your text it’ll be labeled “positive”, for example, and if the word “difficult” appears in your text it’ll be labeled “negative.”

(Rules-based sentiment analysis is more nuanced than what we’ve indicated here, but this is the basic idea.)

The second approach is based on machine learning. A sentiment analysis algorithm will be shown many examples of labeled sentiment data, from which it will learn a pattern that can be applied to new data the algorithm has never seen before.

Of course, there are tradeoffs to both approaches. The rules-based approach is relatively straightforward, but is unlikely to be able to handle the sorts of subtleties that a really good machine-learning system can parse.

Though machine learning is more powerful, however, it’ll only be as good as the training data it has been given; what’s more, if you’ve built some monstrous deep neural network, it might fail in mysterious ways or otherwise be hard to understand.

Supercharge Your Contact Center with Generative AI

Like used car salesmen or college history teachers, contact center managers need to understand the ways in which technology will change their business.

Machine learning is one such profoundly-impactful technology, and it can be used to automatically sort incoming messages by sentiment or priority and generally make your agents more effective.

Realizing this potential could be as difficult as hiring a team of expensive engineers and doing everything in-house, or as easy as getting in touch with us to see how we can integrate the Quiq conversational AI platform into your company.

If you want to get started quickly without spending a fortune, you won’t find a better option than Quiq.

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How Large Language Models Have Evolved

Posted on October 4, 2023October 20, 2025 The Quiq Team

Key Takeaways

The rise of large language models rests on three key pillars: neural networks, the deep learning revolution, and the explosion of large-scale data.
As models grow, they sometimes exhibit unexpected “emergent” abilities that weren’t explicitly trained – suggesting there are non-linear thresholds in capability.
Emergence is not strictly tied to size: in some cases, smaller or higher-quality models show similar behaviors. The precise factors and thresholds for emergence remain an open research area.
LLMs are becoming central to enterprise applications, and their continued evolution – especially with respect to interpretability, safety, and bias – will be critical for future adoption.

In late 2022, large language models (LLMs) exploded into public awareness almost overnight. But like most overnight sensations, the history of large language models is long, fascinating, and informative.

In this piece, we’ll trace the deep evolution of language models and use this as a lens into how they can change your contact center today–and in the future.

Let’s get started!

A Brief History of Artificial Intelligence Development

The human fascination with building artificial beings capable of thought and action goes back a long way. Writing in roughly the 8th century BCE, Homer recounted tales of the Greek god Hephaestus outsourcing repetitive manual tasks to automated bellows and working alongside robot-like “attendants” that were “…golden, and in appearance like living young women.”

Some 500 years later, mathematicians in Alexandria would produce treatises on creating mechanical servants and various kinds of automata. Heron wrote a technical manual for producing a mechanical shrine and an automated theater whose figurines could stage a full tragic play.

Nor is it only ancient Greece that tells similar tales. Jewish legends speak of the Golem, a being made of clay and imbued with life and agency through language. The word “abracadabra”, in fact, comes from the Aramaic phrase “avra k’davra,” which translates to “I create as I speak.”

Through the ages, these old ideas have found new expression in stories such as “The Sorcerer’s Apprentice,” Mary Shelley’s “Frankenstein,” and Karel Čapek’s “R.U.R.,” a science fiction play that features the first recorded use of the word “robot.”

From Science Fiction to Science Fact

But they remained purely fiction until the early 20th Century – a pivotal moment in the history of LLMs – when advances in the theory of computation and the development of primitive computers began to offer a path to building intelligent systems.

Arguably, this really began in earnest with the 1950 publication of Alan Turing’s “Computing Machinery and Intelligence” – in which he proposed the famous “Turing test” – and with the 1956 Dartmouth conference on AI, organized by luminaries John McCarthy and Marvin Minsky.

People began taking AI seriously. Over the next ~50 years in the evolution of large language models, there were numerous periods of hype and exuberance in which major advances were made and long “AI winters” in which funding dried up, and little was accomplished.

Three advances acted to really bring LLMs into their own: the development of neural networks, the deep learning revolution, and the rise of big data. These are important for understanding the history of large language models, so it’s to these that we now turn.

Neural Networks and the Deep Learning Revolution

Walter Pitts and Warren McCulloch laid the groundwork for the eventual evolution of language models in the early 1940s. Inspired by the burgeoning study of the human brain, they wondered if it would be possible to build an artificial neuron with some of the same basic properties as a biological one.

They were successful, though several other breakthroughs would be required before artificial neurons could be arranged into systems capable of doing useful work. One such breakthrough was the discovery of backpropagation in 1960, the basic algorithm still used to train deep learning systems.

It wasn’t until 1985, however, that David Rumelhart, Ronald Williams, and Geoff Hinton used backpropagation in neural networks; in 1989, this allowed Yann LeCun to train such a network to recognize handwritten digits.

Ultimately, it would be these deep neural networks (DNNs) that would emerge from the history of LLMs as the dominant paradigm, but for completeness, we should briefly mention some of the methods that it replaced.

One was known as “rule-based approaches,” and it was exactly what it sounded like. Early AI assistants would be programmed directly with grammatical rules, which were used to parse text and craft responses. This was just as limiting as you’d imagine, and the approach is rarely seen today except in the most straightforward of cases.

Then, there were statistical language models, which bear at least a passing resemblance to the behemoth LLMs that came later. These models try to predict the probability of word n given the n-1 words that came before. If you read our deep dive on LLMs, this will sound familiar, though it was not at all as powerful and flexible as what’s available today.

There were others that are beyond the scope of this treatment, but the key takeaway is that gargantuan neural networks ended up winning the day.

To close this section out, we’ll mention a handful of architectural improvements that came out of this period and would play a crucial role in the evolution of language models. We’ll focus on two in particular: transformers and word vector embeddings.

If you’ve investigated how LLMs work, you’ve probably heard both terms. Transformers are famously intricate, but the basic idea is that they creatively combined elements of predecessor architectures to ameliorate the problems those approaches faced. Specifically, they can use self-attention to selectively attend to key pieces of information in text, allowing them to render higher-fidelity translations and higher-quality text generations.

Word vector embeddings are numerical representations of words that capture underlying semantic information. When interacting with ChatGPT, it can be easy to forget that computers don’t actually understand language, they understand numbers. A word vector embedding is an array of numbers generated with one of several different algorithms, with similar words having similar embeddings. LLMs can process these embeddings to learn enormous statistical patterns in unstructured linguistic data, then use those patterns to generate their own outputs.

All of this research went into making the productive neural networks that are currently changing the nature of work in places like contact centers. The last missing piece was data, which we’ll cover in the next section.

The Big Data Era

Neural networks and deep-learning applications tend to be extremely data-hungry, and access to quality training data has always been a major bottleneck. In 2009 Stanford’s Fei-Fei Li sought to change this by releasing Imagenet, a database of over 14 million labeled images that could be used for free by researchers. The increase in available data, together with substantial improvements in computer hardware like graphical processing units (GPUs), meant that at long last the promise of deep learning could begin to be fulfilled.

And it was. In 2011, a convolutional neural network called “AlexNet” won multiple international competitions for image recognition, IBM’s Watson system beat several Jeopardy! all-stars in a real game, and Apple launched Siri. Amazon’s Alexa followed in 2014, and from 2015 to 2017 DeepMind’s AlphaGo shocked the world by utterly dominating the best human Go players.

All of this set the stage for the rise of LLMs just four short years later.

Where are we Now in the Evolution of Large Language Models?

Now that we’ve discussed this history, we’re well-placed to understand why LLMs and generative AI have ignited so much controversy. People have been mulling over the promise (and peril) of thinking machines for literally thousands of years, and it looks like they might finally be here.

But what, exactly, has people so excited? What is it that advanced AI tools are doing that has captured the popular imagination? In the following sections, we’ll talk about the astonishing (and astonishingly rapid) improvements seen in language models in recent memory.

Getting To Human-Level

One of the more surprising things about LLMs such as ChatGPT is just how good they are at so many different things. LLMs are trained by having them take samples of the text data they’re given, and then trying to predict what words come next given the words that came before.

Modern LLMs can do this incredibly well, but what is remarkable is just how far this gets you. People are using generative AI to help them write poems, business plans, and code, create recipes based on the ingredients in their fridges, and answer customer questions.

What is Emergence in Language Models?

Perhaps even more interesting, however, is the phenomenon of emergence in language models. When researchers tested LLMs on a wide variety of tasks meant to be especially challenging to these models – things like identifying a movie given a string of emojis or finding legal chess moves – they found that in about 5% of tasks, there is a sudden, sharp increase in ability on a given task once a model reaches a certain size.

At present, it’s not really clear how we should think about emergence. One hypothesis for emergence is that a big enough model is able to learn some general piece of knowledge not attainable by a smaller sibling, while another, more prosaic one is that it’s a relatively straightforward consequence of the model’s internal statistical machinery.

What’s more, it’s difficult to pin down the conditions required for emergence in language models. Though it generally appears to be a function of model size, there are cases in which the same abilities can be achieved with smaller models, or with models trained on very high-quality data, and emergence shows up at different scales for different models and tasks.

Whatever ends up being the case, it’s clear that this is a promising direction for future research. Much more work needs to be done to understand how precisely LLMs accomplish what they accomplish. This will not only redound upon the question of emergence, it will also inform the ongoing efforts to make language models safer and less biased.

LLM Agents

One of the bigger frontiers in LLM research is the creation of agents. ChatGPT and similar platforms can generate API calls and functioning code, but humans still need to copy and paste the code to actually do anything with it.

Agents are meant to get around this limitation. Auto-GPT, for example, pairs an underlying LLM with a “bot” that takes high-level tasks, breaks them down into tasks an LLM can solve, and stitches together those solutions.

This work is still in its infancy, but it continues to be very promising.

Multimodal Models

Another development worth mentioning is the rise of multi-modality. A model is “multi-modal” when it can process more than one kind of information, like images and text.

LLMs are staggeringly good at producing coherent language, and image models could do the same thing with images, but now a lot of time and effort is being spent on combining these two kinds of functionality.

The result has been models able to find specific sections of lengthy videos, generate images to accompany textual explanations, and create their own incredible videos from short, simple prompts.

It’s too early to tell what this will mean, but it’s already impacting branding, marketing, and related domains.

What’s Next For Large Language Models?

As with so many things, the meteoric rise of LLMs was presaged by decades of technical work and thousands of years of thought and speculation. In just a few short years, it has become the strategic centerpiece for contact centers the world over.

If you want to get in on the action, you could start by learning more about how Quiq builds customer-facing AI assistants using LLMs. This will provide the context you need to make the wisest decision about deploying this remarkable technology.

Frequently Asked Questions (FAQs)

What are large language models (LLMs)?

Large language models are advanced AI systems trained on massive text datasets to understand and generate human-like language. They use deep learning and neural network architectures to perform tasks like writing, summarizing, and answering questions.

What enabled the rapid evolution of LLMs?

Three breakthroughs fueled their growth: improved neural network design, advances in deep learning algorithms, and access to large-scale, high-quality data that allows for more accurate and context-aware outputs.

What does “emergence” mean in large language models?

Emergence refers to the unexpected behaviors or abilities that appear when a model reaches a certain scale – such as reasoning, understanding context, or solving problems it wasn’t explicitly trained to handle.

Do larger models always perform better?

Not necessarily. While scale often improves performance, some smaller models can show similar emergent abilities when trained with higher-quality data or more efficient architectures.

Why do large language models matter for businesses?

LLMs are transforming enterprise operations – from automating customer support to generating insights – by enabling faster, smarter, and more natural interactions between humans and technology.

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