I am already familiar with some text (document) classification algorithms after processing into a Bag-of-words & tf-idf representation. Classification algorithms such as Multionmial Naive Bayes, Logistic regression, Support vector machine, etc. I also looked into word2vec and not sure if it's suitable for what I am trying. I need something more than just a tf-idf computation, I want to classify based on the semantic relations in the text and I will provide an example.
Let's assume as a simple case I want to predict if a movie is receiving a good review or a bad one, for example the movie "A.I.: Artificial Intelligence". But the sentence is usually composed of one or move movies:
Sentence example: "I really disliked the movie "Ex Machina", but "A.I.: Artificial Intelligence" was quite good!"
Thus inside the sentence I only want to find the relation with the movie "A.I.: Artificial Intelligence" and I don't care at all about other movies. But traditional algorithms could interpret this as a bad review because they capture the words not related to "A.I.: Artificial Intelligence".
So my question is what is the best way to handle such cases.