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 to understand the emotions expressed on social networks.
But how does it work?
Sentiment analysis is a technique from 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, 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 the launch of a product to presidential elections. For this, artificial intelligence uses machine learning models trained with vast 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, like the sentence “I loved this movie, it was amazing!” tends to be classified as positive. While “The service was terrible” is interpreted as negative. More neutral sentences, 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 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 adaptations.
- Context: The same word can have different meanings depending on the usage. “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 full context of sentences.
By using technology, companies can conduct sentiment analysis to monitor the reputation of their brands 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 the mood of the electorate to adjust speeches and strategies. Additionally, automated customer service systems already use this technology to prioritize more urgent or critical messages. Even public health organizations monitor social media to detect disease outbreaks based on mentions of symptoms.
But like any technology, it may have its downside, and here it would be no different. Despite being useful, sentiment analysis with AI is not perfect. Linguistic ambiguity, fake news, and content manipulation can distort the results. In addition, 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 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 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 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
