Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks

• URL: http://arxiv.org/abs/2405.06499v1
• Date: Fri, 10 May 2024 14:23:43 GMT
• Title: Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks
• Authors: Haifa Alrdahi, Riza Batista-Navarro,
• Abstract summary: This study investigates the feasibility of using a modified sentiment analysis method as a means for evaluating chess moves based on text. By extracting insights from move-action phrases, our approach aims to provide a more fine-grained and contextually aware chess move'-based sentiment classification.
• Score: 3.652509571098292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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