I was wondering if it is just because of the computational power and time required to train ML algorithms
It is not because of that; it is arguably because a long and structured text may probably contain segments of "positive" sentiment along with "negative" ones, it can be infinitely more subtle and nuanced, and in principle trying to simply label it overall as "positive/negative" (or even adding a couple more sentiment categories) is futile, unproductive, and at the end of the day hardly useful.
Andrew Ng has famously said:
If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.
and this is exactly the idea behind sentiment analysis: for short text excerpts, and especially for the kinds of text sentiment analysis is usually deployed for (reviews, tweets etc), a typical person has no difficulty in classifying them into such a short list of possible sentiments; additionally, this is a task we want to automate, so that we can do it massively and in scale without having to put a person going through them one by one (not a scalable approach).
These are requirements that normally do not apply to long and structure texts, like (long) newspaper articles, essays etc.; and in these cases, it is not unheard of for people reading them to disagree if, overall, they are "positive", "negative", supportive, contrarian etc (you get the idea), so any thought of actually delegating such a task to an ML model is actually beyond consideration, at least for the present, and not for lacking computing power.