Actually, I’m pretty familiar with the word: sentiment. Last semester, I took Prof. Yu’s Text Mining class, in which sentiment analysis is one of the main components. I still remember that Prof Yu introduced two online sentiment analysis softwares, and let us manually label 50 pieces of online movie comments. Three major sentiments are positive, neutral and negative.
Different from sentiment, semantics is kind of complex in the natural language processing. Also, different field has different analysis code. For example, semantics in linguistics is to study the common law and the similarity/ differences in different languages. While in logistics, semantics is the explanation of logistic system. In other words, it is just like values, which also includes >,<,!=, 1, 0, etc. In computer science, semantics comes to machines’ understanding about natural language.
From my perspective, these two concepts have similarities, particularly in NLP aspect. Tokenisation, stemmer, stopwords, and even decision trees are general approaches no matter you want to study sentiment or semantics. However, sentiment is relatively easier than semantics, since language is very abstruce and complicated to process or analyze by human beings, let alone machine.