Como você pode usar a análise de sentimentos para estudar a ironia na comunicação online?

Verbal irony is the easiest to detect using sentiment analysis, although the units in the preceding and following sentences also need to be analysed. Situational and dramatic irony are too context-dependent, and it is yet to be determined how to analyse it without considerable research limitations.

I'm not an expert on this topic, but my experience with a PSU client in India has given me insight into the complex but intriguing task. The diverse linguistic landscape of India adds layers of complexity to this challenge. Our effective solution involved using multi-lingual NLP models to navigate this variety of languages. Contextual and linguistic analysis were also crucial for capturing the nuances of communication. Additionally, integrating user feedback and expert insights was key to refining our approach. Ultimately, we found that the most effective strategy combined automated analysis with manual review, striking a balance between the efficiency of technology and the nuanced understanding of human review.

A ironia, marcada pela discrepância entre o significado literal e intencionado, desafia a análise tradicional, mas o neuromarketing permite explorar reações além das palavras. Usando inteligência artificial e aprendizado de máquina, padrões de linguagem irônica são identificados, combinando dados textuais, neurológicos e comportamentais. Isso oferece insights mais profundos sobre a percepção e processamento da ironia em ambientes online, melhorando a comunicação e as estratégias de marketing.

While sentiment analysis helps understand the overall emotion conveyed, detecting irony in online communication requires a more nuanced approach. Performing a contextual analysis to comprehend the meaning of words and phrases, particularly those with multiple meanings or a sarcastic tone is one effective way. Another method to determine the tone of a text is by analyzing the use of emoticons, emojis, and punctuation marks, as they can provide additional context. There may be specific emoticons or punctuation patterns that indicate irony.

La ironía es uno de los recursos más frecuentes de los que se sirven los hablantes para expresar descontento e insatisfacción hacia un individuo, un objeto o una circunstancia. Un rasgo inherente a la ironía es que conlleva la digresión semántica del ente referenciado, por lo que la detección, decodificación y tipificación de la ironía es una misión que requiere la experticia del lingüista en las facetas cultural, social e instrumental discursivas y del lenguaje. Los sólidos conocimientos en semántica, sintaxis, pragmática y fonética conforman, indudablemente, una primera base —la base teórica— para operar con precisión en el análisis de fenómenos discursivos y culturales, como la ironía.

There is a simple rule. The more obvious something seems to you, the harder it will be for machines. Walking for example comes second nature to us, but is extremely hard to implement in robots. The same goes for irony. Simple for you, possible, but very hard to learn for machines. Sentiment analysis is still in its infancy because of these very challenges. However, with larger models and datasets, we are getting closer to something I would call "emotionally understanding AI", an AI that actually gets the nonverbal emotional messages always written between lines.

Lexicon-based approaches have their drawbacks, because the units included in lexicons are isolated, and may turn out not to be ironic in a different context.

Be cautious to use AI-based sentiment analysis for any use case - under EU AI Act emotion recognition systems are considered high-risk