Sentiment analysis

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. The objective and challenges of sentiment analysis can be shown through some simple examples. Coronet has the best lines of all day cruisers. Bertram has a deep V hull and runs easily through seas. Pastel-colored 1980s day cruisers from Florida are ugly. I dislike old cabin cruisers. I do not dislike cabin cruisers. (Negation handling) Disliking watercraft is not really my thing. (Negation, inverted word order) Sometimes I really hate RIBs. (Adverbial modifies the sentiment) I'd really truly love going out in this weather! (Possibly sarcastic) Chris Craft is better looking than Limestone. (Two brand names, identifying the target of attitude is difficult). Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names). The movie is surprising with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains). You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains) I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise) Next week's gig will be right koide9! ("Quoi de neuf?", French for "what's new?". Newly minted terms can be highly attitudinal but volatile in polarity and often out of known vocabulary.

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