  We present SentiMATE, a novel end-to-end Deep Learning model for Chess,
employing Natural Language Processing that aims to learn an effective
evaluation function assessing move quality. This function is pre-trained on the
sentiment of commentary associated with the training moves and is used to guide
and optimize the agent's game-playing decision making. The contributions of
this research are three-fold: we build and put forward both a classifier which
extracts commentary describing the quality of Chess moves in vast commentary
datasets, and a Sentiment Analysis model trained on Chess commentary to
accurately predict the quality of said moves, to then use those predictions to
evaluate the optimal next move of a Chess agent. Both classifiers achieve over
90 % classification accuracy. Lastly, we present a Chess engine, SentiMATE,
which evaluates Chess moves based on a pre-trained sentiment evaluation
function. Our results exhibit strong evidence to support our initial hypothesis
- "Can Natural Language Processing be used to train a novel and sample
efficient evaluation function in Chess Engines?" - as we integrate our
evaluation function into modern Chess engines and play against agents with
traditional Chess move evaluation functions, beating both random agents and a
DeepChess implementation at a level-one search depth - representing the number
of moves a traditional Chess agent (employing the alpha-beta search algorithm)
looks ahead in order to evaluate a given chess state.
