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One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency graph parsing task. Although we can find in the literature how transition-based algorithms excel in different dependency graph parsing tasks in terms of accuracy and efficiency, all proposed attempts to tackle SSA following that approach were based on graph-based models. In this article, we present the first transition-based method to address SSA as dependency graph parsing. Specifically, we design a transition system that processes the input text in a left-to-right pass, incrementally generating the graph structure containing all identified opinions. To effectively implement our final transition-based model, we resort to a Pointer Network architecture as a backbone. From an extensive evaluation, we demonstrate that our model offers the best performance to date in practically all cases among prior dependency-based methods, and surpasses recent task-specific techniques on the most challenging datasets. We additionally include an in-depth analysis and empirically prove that the average-case time complexity of our approach is quadratic in the sentence length, being more efficient than top-performing graph-based parsers.<\/jats:p>","DOI":"10.1007\/s10462-025-11463-9","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T04:14:55Z","timestamp":1768018495000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structured sentiment analysis as transition-based dependency graph parsing"],"prefix":"10.1007","volume":"59","author":[{"given":"Daniel","family":"Fern\u00e1ndez-Gonz\u00e1lez","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"issue":"11","key":"11463_CR1","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.3390\/electronics10111348","volume":"10","author":"MA Alonso","year":"2021","unstructured":"Alonso MA, Vilares D, G\u00f3mez-Rodr\u00edguez C, Vilares J (2021) Sentiment analysis for fake news detection. 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