Have you ever wondered how big brands know what consumers are feeling about a product, a campaign, or even a recent event? Well, it seems like magic, but the answer lies in sentiment analysis, an artificial intelligence (AI)-powered technology that has become an essential tool for understanding emotions expressed on social media.
But how does it work?
Sentiment analysis is a technique in the field of natural language processing (NLP), a branch of AI, that seeks to identify, extract, and classify opinions expressed in texts. In other words, it “reads” what you post online and tries to interpret whether you are being positive, negative, or neutral about a subject.
This technique is widely used on platforms like Twitter, Instagram, Facebook, and even in YouTube video comments or Google reviews. Companies, governments, research institutions, and marketing professionals use this tool to measure consumer “mood” on the internet about various topics, from product launches to presidential elections. To do this, artificial intelligence uses machine learning models trained with vast amounts of data. This data includes examples of texts already labeled as “positive,” “negative,” or “neutral,” helping the system learn linguistic patterns associated with different emotions.
To understand in practice, we can use examples, such as the sentence “I loved this movie, it was amazing!” tends to be classified as positive. Meanwhile, “The service was terrible” is interpreted as negative. More neutral phrases, like “I received the product today”, do not carry explicit emotion and are classified as neutral. But it’s not as simple as it seems, as AI also needs to handle challenges such as:
- Irony and sarcasm: Phrases like “Wow, what great service… not” confuse less advanced models.
- Slang and regionalisms: Informal terms vary widely from region to region and require adaptations.
- Context: The same word can have different meanings depending on usage. “Cold,” for example, can describe temperature or a person’s behavior.
To handle these complexities, the most modern solutions use deep neural network-based models like BERT and GPT (including GPT-4), which analyze the full context of sentences.
With the use of this technology, companies can perform sentiment analysis to monitor their brand reputation in real time. If a newly launched product starts receiving criticism on social media, the company can react quickly, avoiding larger crises. During election campaigns, parties analyze voter sentiment to adjust speeches and strategies. Additionally, automated customer service systems already use this technology to prioritize more urgent or critical messages. Even public health agencies monitor social media to detect disease outbreaks based on symptom mentions.
But like any technology, there can be drawbacks, and this is no exception. Despite its usefulness, AI-powered sentiment analysis is not perfect. Linguistic ambiguity, fake news, and content manipulation can distort results. Additionally, there are ethical discussions about privacy and digital surveillance, as these systems analyze user data, often without their knowledge. For this reason, results should be interpreted with caution and human supervision. AI is a powerful tool but still requires the critical and contextual touch of experienced analysts.
With the advancement of generative AI technologies and multimodal models (which understand text, images, audio, and video together), sentiment analysis is expected to become increasingly precise and sophisticated. Soon, it will be possible not only to understand what people say but also how they say it—taking into account tone of voice, facial expressions, and even pauses in speech.
The internet is a vast mirror of human behavior, and sentiment analysis, with the help of artificial intelligence, is learning to decipher this reflection with increasing clarity.
By Gleyber Rodrigues, AI, Strategy, Technology, and Authority Marketing expert
