  Learning chess strategies has been investigated widely, with most studies
focussing on learning from previous games using search algorithms. Chess
textbooks encapsulate grandmaster knowledge, explain playing strategies and
require a smaller search space compared to traditional chess agents. This paper
examines chess textbooks as a new knowledge source for enabling machines to
learn how to play chess -- a resource that has not been explored previously. We
developed the LEAP corpus, a first and new heterogeneous dataset with
structured (chess move notations and board states) and unstructured data
(textual descriptions) collected from a chess textbook containing 1164
sentences discussing strategic moves from 91 games. We firstly labelled the
sentences based on their relevance, i.e., whether they are discussing a move.
Each relevant sentence was then labelled according to its sentiment towards the
described move. We performed empirical experiments that assess the performance
of various transformer-based baseline models for sentiment analysis. Our
results demonstrate the feasibility of employing transformer-based sentiment
analysis models for evaluating chess moves, with the best performing model
obtaining a weighted micro F_1 score of 68%. Finally, we synthesised the LEAP
corpus to create a larger dataset, which can be used as a solution to the
limited textual resource in the chess domain.
