Have you ever wondered how big brands know what consumers are feeling about a product, a campaign, or even a recent event? That's right, it seems magical, but the answer lies in sentiment analysis, a technology powered by artificial intelligence (AI) that has become an essential tool for understanding the emotions expressed on social media.
But how does this work?
Sentiment analysis is a technique in the field of natural language processing (NLP), a branch of AI, that aims to identify, extract, and classify opinions expressed in texts. In other words, she "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 such as Twitter, Instagram, Facebook, and even in comments on YouTube videos or reviews on Google. Companies, governments, research institutions, and marketing professionals use this tool to measure the consumer's "mood" on the internet regarding various topics, from product launches to presidential elections. For this, artificial intelligence uses machine learning models that are trained with enormous amounts of data. These data include 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 sentenceI loved this movie, it was incredible!tends to be classified as positive. AlreadyThe service was terrible.is interpreted as negative. More neutral phrases, such asI received the product todaythey do not carry explicit emotion and are classified as neutral. But it's not as simple as it seems, since AI also has to deal with challenges such as:
- Irony and sarcasm:Phrases likeWow, what great service... just kiddingThey 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 its use. "Cold," for example, can describe temperature or a person's behavior.
To handle these complexities, the most modern solutions use models based on deep neural networks, such as BERT and GPT (including GPT-4), which analyze the full context of the sentences.
With the use of 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 respond quickly, avoiding larger crises. During election campaigns, parties analyze voter sentiment to adjust speeches and strategies. Furthermore, automated customer service systems already use this technology to prioritize more urgent or critical messages. Até mesmo órgãos de saúde pública monitoram redes sociais para detectar surtos de doenças com base em menções de sintomas.
But as with all technology, there would be its drawbacks here as well. Although useful, AI sentiment analysis is not perfect. Linguistic ambiguity, fake news, and content manipulation can distort the results. Furthermore, there are ethical discussions about privacy and digital surveillance, as these systems analyze user data, often without their knowledge. For this reason, the results should be interpreted with caution and human supervision. AI is a powerful tool, but it still needs the critical and contextual touch of experienced analysts.
With the advancement of generative AI technologies and multimodal models (which understand text, image, audio, and video together), sentiment analysis is expected to become increasingly accurate 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 great 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, specialist in AI, Strategy, Technology, and Authority Marketing
