Have you ever wondered how big brands know what consumers are feeling about a product, a campaign, or even a recent event? Yes, it seems like magic, but the answer lies in sentiment analysis, a technology powered by artificial intelligence (AI) that has become an essential tool for understanding emotions expressed on social media.
But how does it work?
Sentiment analysis is a technique within 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 attempts to interpret whether you are being positive, negative, or neutral about a topic.
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 gauge online consumer sentiment on a variety of topics, from product launches to presidential elections. To achieve this, artificial intelligence uses machine learning models trained on massive 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. Already “The service was terrible” is interpreted as negative. More neutral phrases, such as “I received the product today”, do not convey explicit emotion and are classified as neutral. But it’s not as simple as it seems, as AI also needs to deal with challenges such as:
- Irony and sarcasm: Phrases like “Wow, what a great service… but not really.” confuse less advanced models.
- Slang and regionalisms: Informal terms vary greatly from region to region and require adaptations.
- Context: The same word can have different meanings depending on how it’s used. “Cold,” for example, can describe a person’s temperature or behavior.
To address these complexities, state-of-the-art solutions use models based on deep neural networks, such as BERT and GPT (including GPT-4), which analyze the full context of sentences.
Using technology, companies can perform sentiment analysis to monitor their brand reputation in real time. If a newly launched product starts receiving criticism online, the company can react quickly, avoiding major crises. During election campaigns, political parties analyze the electorate’s mood to adjust their speeches and strategies. Furthermore, automated customer service already uses 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 as with any technology, there are drawbacks, and this one is no exception. While useful, AI-powered sentiment analysis isn’t perfect. Linguistic ambiguity, fake news, and content manipulation can distort results. Furthermore, there are ethical discussions surrounding privacy and digital surveillance, as these systems analyze user data, often without their knowledge. For this reason, results must be interpreted with caution and human oversight. AI is a powerful tool, but it 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 accurate and sophisticated. Soon, it will be possible to understand not only 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 great mirror of human behavior, and sentiment analysis, with the help of artificial intelligence, is learning to decipher this reflection with ever greater clarity.
By Gleyber Rodrigues, AI, Strategy, Technology and Authority Marketing Specialist
