AI for Social Listening

How AI Can Change Your Social Listening Practice

Since the release of OpenAI’s generative models in late 2022, generative AI has become a buzzword in marketing. However, long before this, AI was the backbone of social listening tools, helping brands extract insights from massive volumes of social media data. These tools initially used traditional NLP techniques to analyze sentiment, detect themes, and recognize entities—turning raw data into measurable insights. Now, with generative AI, social listening has taken another leap forward, enabling faster, richer insights and more accessible analysis. Let’s dive into the evolution of AI in social listening and explore how generative models are shaping the future of this field.

A History Of Artificial Intelligence + Machine Learning in Social Listening

The term "Artificial Intelligence" exploded onto the scene in November 2022 with the launch of ChatGPT (an event we covered with an insight brief at the time). While ChatGPT democratized artificial intelligence (and made it much easier for non-technical people to access and understand), AI is not a new concept.

Figure 1: Popularity on social media for the term "Machine Learning" versus the term "Artificial Intelligence;" (November 2014 through November 2024); Infegy Social Dataset.

Social listening tools, like Infegy Starscape, Infegy Atlas, and Infegy's Social Radar (our first product), have used AI-enabled technology to analyze massive amounts of textual data. The field of computer science and information science is known as natural language processing, known as NLP. At its core, natural language processing uses computers to analyze large amounts of written or spoken language, doing things that a human would do automatically.

Early companies like Radian6, Sysomos, and Crimson Hexagon set the stage for brands to monitor online conversations by tracking keywords across social media. These first-generation tools provided basic volume metrics and keyword mentions, but they couldn't interpret nuances in language, making insights limited and often superficial. As NLP technologies advanced, companies like Infegy, Talkwalker, and Brandwatch emerged, using sophisticated machine-learning models to push beyond simple keyword tracking.

When you use an Infegy product, you use Infegy AI. Infegy AI includes software that tackles the most important natural language processing challenges like sentiment analysis or named entity recognition. With NLP capabilities, advanced tools like Infegy Starscape can now understand sentiment, identify key entities like people and brands, detect themes, and even uncover cultural trends.

Sentiment Analysis: Infegy's AI For Understanding Emotion In Text

Sentiment analysis is an AI-powered process in Infegy's natural language processing (NLP) suite that detects the emotional tone behind text. Using advanced machine learning, Infegy products like Starscape and Atlas classify content as positive, negative, neutral, or more complex emotional states, offering a clear view of audience sentiments.

Figure 2: Sentiment associated with the term "Artificial Intelligence" (November 2014 through November 2024); Infegy Social Dataset.

Named Entity Recognition: AI For Identifying Key Entities

Named Entity Recognition (NER) is a core feature of Infegy's AI-powered tools, designed to identify and categorize important entities—such as people, brands, and organizations—within textual data. Infegy's products, including Starscape and Atlas, leverage machine learning to recognize these entities accurately across vast datasets, enabling brands to pinpoint relevant topics and key players in public conversations quickly.

Figure 3: Top Entities associated with the term "Artificial Intelligence" (November 2014 through November 2024); Infegy Social Dataset.

What Is Generative AI, And How Do Social Listening Tools Use It?

So far, while talking about traditional NLP methods and AI applications, we haven't discussed tools like ChatGPT and the generative models underpinning it. Generative AI represents a groundbreaking advancement in artificial intelligence, enabling systems to create entirely new content—from text and images to audio and video—based on patterns learned from massive datasets. Unlike traditional NLP, which focuses on analyzing and interpreting existing text, generative models like ChatGPT use deep learning techniques to generate coherent, contextually relevant responses, write articles, or create “art.” These models leverage large language models (LLMs), which can generate human-like responses and adapt to varied tasks, marking a new era in AI where machines can understand and produce original content.

How Generative AI Changed Social Listening

With the growth of Generative AI models, social listening companies rushed to incorporate the technology into their platforms. These early iterations were limited by the very small context windows associated with the early models. As a result, these early models struggled with meaningful, long-form conversations and nuanced insights due to their limited context windows.

Infegy AI takes a different approach. We pass all of the analytics we talked about before, like sentiment analysis, entities, content subjects, key topics, and narratives, into these models. As a result, we can pass a shorter input and use the generative AI models for summarizing, something researchers say they are best at.

Figure 4: Video showing how Infegy’s Generative AI summary widgets work; Infegy Social Dataset.

AI Social Listening Example

We’ve discussed what AI can do, but what does an example look like?

Imagine you’re analyzing social conversations about artificial intelligence. Without generative AI, the results might include sentiment analysis and quantifiable insights that show a positive tone. Traditional AI can identify words like "future," "world," or "business" as positive associations, but it doesn’t fully explain why people feel this way. Without domain expertise, you’d likely need to conduct time-consuming side research to understand the specific context behind these associations—such as AI's potential in different industries or its broader implications for the workforce. Generative AI, however, bridges this gap by offering more detailed insights that reveal not only what people are saying but also the underlying reasons driving their sentiment.

Figure 5: Traditional Social Listening Dashboard around the Topic of Artificial Intelligence (November 2014 through November 2024); Infegy Social Dataset.

Infegy’s generative AI tools deliver powerful summaries that reveal both the content and context of social conversations. For instance, when analyzing discussions on artificial intelligence, Infegy’s models go beyond sentiment and themes to capture nuanced insights into AI’s evolution, societal impact, and ethical considerations. This allows brands and analysts to understand not only what people are discussing but also the reasons and emotions behind their views—without requiring extensive domain expertise.

Figure 6: Infegy’s Generative AI Widget (November 2014 through November 2024); Infegy Social Dataset.

Takeaways For Your Brand