How can sentiment analysis cope with the evolving nature of language and slang?

It all comes down to maintaining up to date detailed ontologies with phrases aligned to the spectrum of sarcasm and irony. We need to remember that the detection algorithms in text are only as good as the contextual intelligence programmed in to it. So to detect sarcasm and irony, we need to look at samples of sarcastic language (that we know is sourced by either being written by ourselves or a credible sarcastic source), and then use text mining to look for word patterns around the entire paragraph of sentences. We can then program those detection rules by sentence and word type and frequency occurrence to tag sarcastic sentiment. The same process applies to irony. The best place to pick up the latest would be social media.

I truly believe that since the beginning we have done giant steps in the right direction and we can finally have some models that let us engage more in conversations, with a variety of topics, ambiguities and different contexts. Language models such as BERT, GPT-x, LaMDA and others let us build tools that can really engage in a free-flowing way about different topics, changing from one topic to another almost like a human would do. Of course, there is room for improvements, the human nature is extraordinarily complex, but I see this as a piece of a puzzle, along with other external features that explain better situations, events, external factors, that words can't directly express.

Sentiment analysis remains an aspirational goal that has been pursued by marketing and public relations firms for many years now. Currently sentiment analysis is hampered by the lack of theoretical input from disciplines such as linguistics, semantics, narrative studies, psychology and anthropology. The sentiment value of any utterance is derived from its codified meaning (lexical/referential) and its uncodified meanings (connotational, narrative, discursive, presuppositional, cultural). We need more sophisticated approaches to modelling sentiment that account for the factors influencing the receivers' interpretations of utterances and describe the possible ranges of their interpretations .

Sentiment analysis copes with emotion and intensity in a text by using different approaches to capture the strength of feelings. It can use predefined word lists or lexicons that rate words based on their emotional weight. Machine learning and deep learning models are also used to understand the context and tone of the text and help to detect the level of emotion more accurately. These models look for patterns in how people express feelings like happiness or anger. However, the effectiveness of these methods depends on the quality of the training data. The models must be trained from diverse sources to better handle complex emotions and intensity.

Sentiment analysis can adapt to the constantly changing language and slang by using more flexible and updated data sources. This includes social media posts and online forums where new words or phrases often appear. By regularly adding new words, emojis or slang into its system, sentiment analysis can better understand and analyze modern expressions. It may also rely on user-generated content to stay relevant and recognize how people use language creatively. However, ensuring the reliability of these sources is important to avoid adding errors or bias. As language evolves, sentiment analysis needs to be evolving by learning from new patterns in speech and text.