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, a technology powered by artificial intelligence (AI) that has become an essential tool for understanding the emotions expressed on social media.
But how does that work?
Sentiment analysis is a technique within 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 such as 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 that are trained with enormous 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 this in practice, we can use examples, such as the phrase "I loved this movie, it was amazing!" which tends to be classified as positive. Conversely, "The service was terrible" is interpreted as negative. More neutral phrases, such as "I received the product today ," don't carry explicit emotion and are classified as neutral. But it's not as simple as it seems, since AI also needs to deal with challenges such as:
- Irony and sarcasm: Phrases like "Wow, what great service... NOT!" confuse less advanced models.
- Slang and regionalisms: Informal terms vary greatly from region to region and require adaptation.
- Context: The same word can have different meanings depending on its use. "Cold," for example, can describe temperature or a person's behavior.
To deal with these complexities, the most modern solutions use models based on deep neural networks, such as BERT and GPT (including GPT-4), which analyze the complete context of sentences.
By using 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 their speeches and strategies. Furthermore, 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 mentions of symptoms.
But like all technologies, this one has its drawbacks. While useful, AI-powered sentiment analysis isn't perfect. Linguistic ambiguity, fake news, and content manipulation can distort the results. Furthermore, there are ethical discussions about privacy and digital surveillance, since these systems analyze user data, often without their knowledge. For this reason, the results should be interpreted with caution and human oversight. AI is a powerful tool, but it still needs the critical and contextual input 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.
