LICD: A Language-Independent Approach for Aspect Category Detection

Ghadery, Erfan; Movahedi, Sajad; Sabet, Masoud Jalili; Faili, Heshaam; Shakery, Azadeh

doi:10.1007/978-3-030-15712-8_37

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…A logistic regression model is then trained with such features to make the prediction. Later methods further leverage different characteristics of the task to improve the performance, e.g., using attention mechanism to attend to different parts of the text for different categories [38], considering the word-word co-occurrence patterns [118], and measuring the text matching between the sentence and a set of representative words in each specific category to predict whether a category exists [39].…”

Section: Aspect Category Detection (Acd)mentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks have been introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

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Section: Aspect Category Detection (Acd)mentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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“…to improve the aspect category detection performance. Alghunaim [2015] followed a similar approach as Kiritchenko et al [2014] by using an SVM classiier on features such as the number of tokens per review, category similarity score, Normalized Average Vectors (NAV) computed from the word vectors generated using Word2Vec, similarity vectors, and weighted similarity vectors Xenos et al [2016] and Ghadery et al [2019b] tried ensemble methods on the SVM classiier with various kernels functions such as the RBF and polynomial kernels. And Guha et al [2015] experimented with various features and their combinations from the BOW, synsets from the WordNet database, Word2Vec features, TF-IDF features with a C one-vs-all Random Forest classiiers one for each category.…”

Section: Supervised Learning Approachesmentioning

confidence: 99%

Survey on Aspect Category Detection

et al. 2022

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In recent years, aspect category detection has become popular due to the rapid growth in customer reviews data on e-commerce and other online platforms. Aspect Category Detection, a sub-task of Aspect-Based Sentiment Analysis, categorizes the reviews based on the features of a product such as a laptop’s display, or an aspect of an entity such as the restaurant’s ambiance. Various methods have been proposed to deal with such a problem. In this paper, we first introduce several datasets in the community that deal with this task and take a closer look at them by providing some exploratory analysis. Then, we review a number of representative methods for aspect category detection and classify them into two main groups: 1) supervised learning, and 2) unsupervised learning. Next, we discuss the strengths and weaknesses of different kinds of methods, which are expected to benefit both practical applications and future research. Finally, we discuss the challenges, open problems, and future research directions.

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