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For decades, content’s ability to stir emotion has reliably predicted engagement. This interplay forms the bedrock of modern communications, marketing, and public relations strategies. ‘Sentiment’, or the emotional undertone of a text, has become the framework through which we evaluate digital content.
Today, our information landscape is shifting. Humans are stepping down as the gatekeepers of content, and algorithms are filling the void. Advanced machine learning models now govern our social media feeds, news apps, and search engines.
As algorithms reshape our relationship with information, we’re left wondering what factors drive their decision-making – a question especially salient for marketing and communications professionals. Is sentiment a reliable proxy for engagement in the algorithmic age?
How Machine Learning Models Process Sentiment
Machine learning models – especially those involved in content curation and generation – depend heavily on sentiment analysis. Content charged with intense emotion, whether positive or negative, significantly influences their behavior.
Social media and news algorithms, designed to increase user engagement, are trained to detect and interpret sentiment in content. When these models identify content with a strong sentiment bias, they assign it a higher ‘weight’, boosting its visibility in recommendation lists and news feeds. Search engines are another important example. Sentiment-laden content – from e-commerce listings to blog posts – see higher page ranks and amplified visibility.
Generative models like chatbots function similarly, adjusting their outputs based on the sentiment of their training data. To stimulate engagement, these models assign higher ‘weight’ to data with strong sentiment, which then influences their output decisions. Chatbots may also tailor their responses to match the sentiment of user queries.
AI may appear infallible and objective, but it, too, caters to human behavior. Content with intense sentiment, positive or negative, will continue to generate user engagement. And our algorithms will continue to adapt to these tendencies.
Lessons for the Industry
What does this mean for marketing and communications professionals? Will tried-and-tested strategies continue delivering success?
Not necessarily. With AI as the new gatekeeper, our strategies need to evolve. Modern algorithms are opaque, and understanding the relationship between sentiment and AI is just the first step. The challenge lies in identifying what registers as strong sentiment in the eyes of the machine.
The industry has mastered content creation for human audiences, but communicating effectively with AI requires a new approach. In many cases, humans and algorithms interpret language very differently. As AI becomes the new prism through which humans access and absorb information, algorithms must be a focal point of modern marketing and communications strategies. Fortunately, emerging technologies offer the tools to decode algorithmic biases and decision-making patterns.
Natural language processing can help uncover how machine learning models react to different topics and sentiments. These insights are vital to crafting a successful marketing and communications strategy for the algorithmic age.
Literate AI: Your Guide to the Algorithmic Age
At Literate AI, we work to simplify this challenge. Our proprietary AI platform, combined with our team’s decades of expertise, helps demystify the impact of machine learning on your communications, investor relations, and digital strategies.
We’ll show you how algorithm’s interpret your content's sentiment, enabling you to tailor your communications to our new information gatekeepers. For more insights on unlocking value in a world overrun by algorithms, reach out today.
