This relates back to a discussion that was had on Twitter not too long ago. The problem is that “similarity” is ill-defined. You can read through the thread 3. I did not add much but the discussion between Nils Reimers and Yoav Goldberg is interesting.
It is a good mind exercise to think outside of what you’d want it to mean, and what the models are actually paying attention to. In your example, it is likely that content words receive the most attention and are responsible for a lot of the “meaning” (representation vector). On top of that, lexical overlap inevitably contributes to this value as well. That means that “(rainy) days” and “because they make me feel” already overlap. Yes, in terms of semantics and the whole context the meaning is different, but for the model these sentences are very “similar”. These models do not (necessarily) do sentiment analysis and comparison which seems to be what you are after.
You may wish  to look for sentiment models instead.