  Aspect-based sentiment analysis (ABSA) delves into understanding sentiments
specific to distinct elements within a user-generated review. It aims to
analyze user-generated reviews to determine a) the target entity being
reviewed, b) the high-level aspect to which it belongs, c) the sentiment words
used to express the opinion, and d) the sentiment expressed toward the targets
and the aspects. While various benchmark datasets have fostered advancements in
ABSA, they often come with domain limitations and data granularity challenges.
Addressing these, we introduce the OATS dataset, which encompasses three fresh
domains and consists of 27,470 sentence-level quadruples and 17,092
review-level tuples. Our initiative seeks to bridge specific observed gaps: the
recurrent focus on familiar domains like restaurants and laptops, limited data
for intricate quadruple extraction tasks, and an occasional oversight of the
synergy between sentence and review-level sentiments. Moreover, to elucidate
OATS's potential and shed light on various ABSA subtasks that OATS can solve,
we conducted experiments, establishing initial baselines. We hope the OATS
dataset augments current resources, paving the way for an encompassing
exploration of ABSA (https://github.com/RiTUAL-UH/OATS-ABSA).
