  The chess domain is well-suited for creating an artificial intelligence (AI)
system that mimics real-world challenges, including decision-making. Throughout
the years, minimal attention has been paid to investigating insights derived
from unstructured chess data sources. In this study, we examine the complicated
relationships between multiple referenced moves in a chess-teaching textbook,
and propose a novel method designed to encapsulate chess knowledge derived from
move-action phrases. This study investigates the feasibility of using a
modified sentiment analysis method as a means for evaluating chess moves based
on text. Our proposed Aspect-Based Sentiment Analysis (ABSA) method represents
an advancement in evaluating the sentiment associated with referenced chess
moves. By extracting insights from move-action phrases, our approach aims to
provide a more fine-grained and contextually aware `chess move'-based sentiment
classification. Through empirical experiments and analysis, we evaluate the
performance of our fine-tuned ABSA model, presenting results that confirm the
efficiency of our approach in advancing aspect-based sentiment classification
within the chess domain. This research contributes to the area of game-playing
by machines and shows the practical applicability of leveraging NLP techniques
to understand the context of strategic games.
