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1. FineSum: Target-Oriented, Fine-Grained Opinion Summarization

   https://doi.org/10.1145/3539597.3570397  

   Ge, Suyu; Huang, Jiaxin; Meng, Yu; Han, Jiawei (February 2023, ACM)

   Proc. 2023 ACM Int. Conf. on Web Search and Data Mining (Ed.)

   Target-oriented opinion summarization is to profile a target by extracting user opinions from multiple related documents. Instead of simply mining opinion ratings on a target (e.g., a restaurant) or on multiple aspects (e.g., food, service) of a target, it is desirable to go deeper, to mine opinion on fine-grained sub-aspects (e.g., fish). However, it is expensive to obtain high-quality annotations at such fine-grained scale. This leads to our proposal of a new framework, FineSum, which advances the frontier of opinion analysis in three aspects: (1) minimal supervision, where no document-summary pairs are provided, only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to a specific subject or characteristic within each general aspect; and (3) phrase-based summarization, where short phrases are taken as basic units for summarization, and semantically coherent phrases are gathered to improve the consistency and comprehensiveness of summary. Given a large corpus with no annotation, FineSum first automatically identifies potential spans of opinion phrases, and further reduces the noise in identification results using aspect and sentiment classifiers. It then constructs multiple fine-grained opinion clusters under each aspect and sentiment. Each cluster expresses uniform opinions towards certain sub-aspects (e.g., “fish” in “food” aspect) or characteristics (e.g., “Mexican” in “food” aspect). To accomplish this, we train a spherical word embedding space to explicitly represent different aspects and sentiments. We then distill the knowledge from embedding to a contextualized phrase classifier, and perform clustering using the contextualized opinion-aware phrase embedding. Both automatic evaluations on the benchmark and quantitative human evaluation validate the effectiveness of our approach. 

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2. “Misc”-Aware Weakly Supervised Aspect Classification

   Li, Peiran; Guo, Fang; Shang, Jingbo (January 2021, Proceedings of the 2021 SIAM International Conference on Data Mining (SDM))

   null (Ed.)

   Aspect classification, identifying aspects of text segments, facilitates numerous applications, such as sentiment analysis and review summarization. To alleviate the extensive human effort required by existing aspect classification methods, in this paper, we focus on a weakly supervised setting—the model input only contains domainspecific raw texts and a few seed words per pre-defined aspect. We identify a unique challenge here as to how to classify texts without any pre-defined aspects. The existence of this kind of “misc” aspect text segments is very common in review corpora. It is difficult, even for domain experts, to nominate seed words for the “misc” aspect, which makes existing seed-driven text classification methods not applicable. Therefore, we propose to jointly model pre-defined aspects and the “misc” aspect through a novel framework, ARYA. It enables mutual enhancements between pre-defined aspects and the “misc” aspect via iterative classifier training and seed set updating. Specifically, it trains a classifier for pre-defined aspects and then leverages it to induce the supervision for the “misc” aspect. The prediction results of the “misc” aspect are later utilized to further filter the seed word selections for pre-defined aspects. Experiments in three domains demonstrate the superior performance of our proposed framework, as well as the necessity and importance of properly modeling the “misc” aspect 

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3. Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text

   Park, Kunwoo; Pan, Zhufeng; Joo, Jungseock (August 2021, Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021,)

   null (Ed.)

   Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as they do not consider the direction of sentiments expressed between entities. In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction. From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated. We present a simple but effective approach for utilizing a pretrained transformer, which infers the target class by predicting multiple question-answering tasks and combining the outcomes. We demonstrate the utility of our proposed method for social science research questions by analyzing positive and negative opinions between political entities in two major events: 2016 U.S. presidential election and COVID-19. The newly proposed problem, data, and method will facilitate future studies on interdisciplinary NLP methods and applications. 

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4. Against method: Exploding the boundary between qualitative and quantitative studies of science

   https://doi.org/10.1162/qss_a_00056  

   Kang, Donghyun; Evans, James (September 2020, Quantitative Science Studies)

   null (Ed.)

   Quantitative and qualitative studies of science have historically played radically different roles with opposing epistemological commitments. Using large-scale text analysis, we see that qualitative studies generate and value new theory, especially regarding the complex social and political contexts of scientific action, while quantitative approaches confirm existing theory and evaluate the performance of scientific institutions. Large-scale digital data and emerging computational methods could allow us to refigure these positions, turning qualitative artifacts into quantitative patterns into qualitative insights across many scales, heralding a new era of theory development, engagement, and relevance for scientists, policy-makers, and society. 

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5. EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODEL

   https://doi.org/10.1017/pds.2023.186  

   Han, Yi; Bruggeman, Ryan; Peper, Joseph; Ciliotta_Chehade, Estefania; Marion, Tucker; Ciuccarelli, Paolo; Moghaddam, Mohsen (July 2023, Proceedings of the Design Society)

   Aspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design. 

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