Semantic Lexicon Expansion for Concept-Based Aspect-Aware Sentiment Analysis

Coden, Anni; Gruhl, Daniel; Lewis, Neal; Mendes, Pablo N.; Nagarajan, Meena; Ramakrishnan, C. R.; Welch, Steve

doi:10.1007/978-3-319-12024-9_4

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…Query clarification refers to the process of improving a search query by adding more context or details to it. In recent years, query clarification has become an essential task in various domains, such as entity disambiguation [21], voice [29], dialogue [8,49], question answering [9,52,70], recommendation [18,78]. In mixed-initiative search systems where the initiative shifts back and forth between agents and users [5,28], asking clarifying questions has received considerable attention [27,45,56,76].…”

Section: Related Workmentioning

confidence: 99%

Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search

Yuan,

Siro,

Aliannejadi

et al. 2024

Proceedings of the ACM Web Conference 2024

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In mixed-initiative conversational search systems, clarifying questions are used to help users who struggle to express their intentions in a single query. These questions aim to uncover user's information needs and resolve query ambiguities. We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. Therefore, we propose to add images to clarifying questions and formulate the novel task of asking multimodal clarifying questions in open-domain, mixed-initiative conversational search systems. To facilitate research into this task, we collect a dataset named Melon that contains over 4k multimodal clarifying questions, enriched with over 14k images. We also propose a multimodal query clarification model named Marto and adopt a prompt-based, generative fine-tuning strategy to perform the training of different stages with different prompts. Several analyses are conducted to understand the importance of multimodal contents during the query clarification phase. Experimental results indicate that the addition of images leads to significant improvements of up to 90% in retrieval performance when selecting the relevant images. Extensive analyses are also performed to show the superiority of Marto compared with discriminative baselines in terms of effectiveness and efficiency. CCS CONCEPTS• Information systems → Evaluation of retrieval results.

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Section: Related Workmentioning

confidence: 99%

Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search

Yuan,

Siro,

Aliannejadi

et al. 2024

Proceedings of the ACM Web Conference 2024

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“…As such, methods for creating ontologies semi-automatically or even completely automatically can be vital when creating larger ontologies. For example, in [38], an ontology for ABSC is created semi-automatically using a proposed semantic asset management workbench (SAMW). A method for fully automatically creating ontologies is proposed in [8].…”

Section: Ontology-mentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Classification

Brauwers

Frăsincar

2022

ACM Comput. Surv.

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With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

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“…BoW translates input reviews into binary vectors that represent a given word's appearance in selected sentences. To exploit the common domain knowledge information, one can also use a domain ontology for sentiment classification, which can be created manually (Schouten and Frasincar, 2018;Schouten et al, 2017), semi-automatically (Coden et al, 2014;Zhuang et al, 2020), or automatically (Alani et al, 2003). Ontology has achieved success in the information retrieval tasks of different domains.…”

Section: Related Literaturementioning

confidence: 99%

A hybrid approach for aspect-based sentiment analysis using a double rotatory attention model

Frăsincar¹,

Zhou²,

Cheng³

2022

IJWET

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Nowadays, the Web is an essential hub for gathering comments on entities and their associated aspects. In this paper we propose a model which is capable of extracting these opinions and predicting the sentiment scores in aspect-level sentiment mining. In our two-step approach, a lexicalized domain ontology is firstly applied for sentiment classification. If the result is inconclusive from the first step, the backup model Double Rotatory Attention Mechanism is applied, which utilizes deep contextual word embeddings to better capture the (multi-)word semantics in the given text. This study contributes to the current research by introducing novel repetition and rotatory structures to refine the attention mechanism. It is shown that our model outperforms state-of-the-art methods on the datasets of SemEval 2015 and SemEval 2016.

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Semantic Lexicon Expansion for Concept-Based Aspect-Aware Sentiment Analysis

Cited by 11 publications

References 6 publications

Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search

Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search

A Survey on Aspect-Based Sentiment Classification

A hybrid approach for aspect-based sentiment analysis using a double rotatory attention model

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