Aspect Category Detection Research Articles

Survey on Aspect Category Detection

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 article, 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.

Using Multi-Label Multi-Class Support Vector Machines with Semantic and Lexical Features for Aspect Category Detection

In contrast to the aspects, aspect categories are often coarser and don't always appear as terms in sentences. Besides, the typical way to element the types associated with part is generally grainier concerning factors and doesn't exist within verdicts. The primary intent of the study is to investigate the efficacy of Lexicon, linguistic, vector-based, and features correlated to semantics within the aspect of the responsibility built with the finding of aspect category detection ACD). Semantic and emotional data are captured via vector-based features. Further, it examines vector-based feature superiority issues within the compression of features of text-based characteristics. Study purposes to the linguistic efficacy with the Lexicon, linguistic, and semantic features, also vector-based dependent to the system. Also, the information led with vector-based features that capture the semantic with sentimental analysis characteristics. With the experimental outcomes, the performance efficacy with the vector-based features outperformed text-based features. The methodologies associated with deep learning have generated features within the vector orientation relevant to the word-based structures. Therefore, the proposed method achieved effectiveness with the determined constraints by applying the metrics of precision, recall, and F1 scores. Correlating with the performance of ABSA's state-of-the-art techniques, the proposed research process gained superior outcomes.

Boosting aspect category detection with inference heuristics and knowledge enhancement

Aspect category detection (ACD) aims to identify the aspect categories from reviewers’ expressed opinions in a given sentence, where one or multiple predefined aspect categories are mentioned explicitly or implicitly. With the boom of the pretrained language model, related studies have achieved significant improvements in ACD. However, the studies usually follow the canonical method of fine-tuning and neglect deeply mining internal knowledge or incorporating external knowledge, which could lead to suboptimal results. To address this issue, we propose a novel multilevel knowledge-aware ACD model by innovatively converting ACD to a binary sentence-pair classification task from the viewpoint of natural language inference, which is effective and consists of four key components. The model first expands the predefined aspect categories by introducing terms with high semantic similarity scores from commonsense knowledge bases. Next, the model generates synthetic premise-hypothesis sentence pairs based on the aspect categories and an inference heuristic template. Then, the training data are effectively augmented and used for fine-tuning the proposed model. Moreover, the model designs a pooling strategy to mine the rich syntactic and semantic knowledge encoded in the internal layers of BERT. Finally, the pooled low-dimensional representation is fed to a linear classifier to detect aspect categories. Experimental results on the SemEval-2014 and SemEval-2016 benchmark datasets achieve F1-scores of 92.75% and 83.58%, respectively, which demonstrate the superiority of our proposed model compared with some strong baselines.

An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques

Considering customer reviews is one of the challenges of real-world decision models. These reviews can be on different platforms and include a large amount of information and may also include incomprehensible and unrelated phrases. The main advantage of customer reviews is the realistic view that it provides a realistic view of the product or service. Therefore, converting unstructured and incoherent customer-based reviews into machine learning language and ultimately turning it into a decision model is very important. In this paper, we propose an end-to-end ranking method for integrating mechanisms such as text processing, sentiment analysis and the multi-criteria decision-making technique. The proposed ranking method relies on the integration of three methods, namely, the aspect-based sentiment analysis (ABSA) method, the Dawid-Skene algorithm and the Best Worst Method (BWM). In other words, the proposed work encompasses four major steps: i) crawling customer reviews, ii) preprocessing, iii) aspect term extraction, aspect category detection and polarity detection, and iv) designing a decision-making model. The main contribution of this study is to consider ABSA at three levels simultaneously and integrate ABSA and BWM in designing an end-to-end ranking method for ranking the quality of hotel services, facilities and amenities based on customer reviews. The ability of the proposed end-to-end ranking framework is evaluated using a real data set of user reviews of Sydney hotels.

BARLAT: A Nearly Unsupervised Approach for Aspect Category Detection

BARLAT: A Nearly Unsupervised Approach for Aspect Category Detection

Multi-Self-Attention for Aspect Category Detection and Biomedical Multilabel Text Classification with BERT

Multilabel classification is one of the most challenging tasks in natural language processing, posing greater technical difficulties than single-label classification. At the same time, multilabel classification has more natural applications. For individual labels, the whole piece of text has different focuses or component distributions, which require full use of local information of the sentence. As a widely adopted mechanism in natural language processing, attention becomes a natural choice for the issue. This paper proposes a multilayer self-attention model to deal with aspect category and word attention at different granularities. Combined with the BERT pretraining model, it achieves competitive performance in aspect category detection and electronic medical records’ classification.

A Convolutional Stacked Bidirectional LSTM with a Multiplicative Attention Mechanism for Aspect Category and Sentiment Detection

A Convolutional Stacked Bidirectional LSTM with a Multiplicative Attention Mechanism for Aspect Category and Sentiment Detection

Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

Ensemble application of bidirectional LSTM and GRU for aspect category detection with imbalanced data

E-commerce websites produce a large number of online reviews, posts, and comments about a product or service. These reviews are used to assist consumers in buying or recommending a product. However, consumers are expressing their views on a specific aspect category of a product. In particular, aspect category detection is one of the subtasks of aspect-based sentiment analysis, and it classifies a given text into a set of predefined aspects. Naturally, a class imbalance problem occurs in real-world applications. The class imbalance is studied over the last two decades using machine learning algorithms. However, there is very little empirical research in deep learning with the class imbalance problem. In this paper, we propose bidirectional LSTM and GRU networks to deal with imbalance aspect categories. The proposed method applies a data-level technique to reduce class imbalance. Specifically, we employ the stratified sampling technique to deal with imbalanced classes. Moreover, we create word vectors with the corpus-specific word embeddings and pre-trained word embeddings. This word representations fed into the proposed method and their merge modes such as addition, multiplication, average, and concatenation. The performance of this method is evaluated with a confusion matrix, precision, recall, F1-score with micro-average, macro-average, and weighted average. The experimental result analysis suggests that the proposed method outperforms with pre-trained word embeddings.

Multiple perspective attention based on double BiLSTM for aspect and sentiment pair extract

Aspect category sentiment analysis has attracted increasing attention because of its outstanding performance in mining the fine-grained sentiment expression of users. In recent years, a new aspect category and sentiment pair extraction (ASPE) task has been proposed to simultaneously extract aspect categories and sentiment pairs. Most existing research works are designed in a two step pipeline, that is, they first perform aspect category detection, and subsequently conduct aspect category sentiment analysis. However, the pipeline method can clearly lead to error propagation from previous step. In this work, we propose a new framework for multiple perspective attention based on double BiLSTM with a novel joint strategy for ASPE to alleviate the accumulation of errors in the pipeline method. The experimental results on benchmark datasets SemEval and BDCI-2018 demonstrate the effectiveness of the proposed approach in terms of both accuracy and explainability for the ASPE task.

A Similarity Score Model for Aspect Category Detection

Aspect-based Sentiment Analysis (ABSA) aims to extract significant aspects of an item or product from reviews and predict the sentiment of each aspect. Previous similarity methods tend to extract aspect categories at the word level by combining Language Models (LM) in their models. A drawback for the LM model is its dependence on a large amount of labelled data for a specific domain to function well. This work proposes a mechanism to address labelled data dependency by a one-step approach experimenting to decide the best combinatory architectures of recurrent-based LM and the best semantic similarity measures for fostering a new aspect category detection model. The proposed model addresses drawbacks of previous aspect category detection models in an implicit manner. The datasets of this study, S1 and S2, are from standard SemEval online competition. The proposed model outperforms the previous baseline models in terms of the F1-score of aspect category detection. This study finds more relevant aspect categories by creating a more stable and robust model. The F1 score of our best model for aspect category detection is 79.03% in the restaurant domain for the S1 dataset. In dataset S2, the F1-score is 72.65% in the laptop domain and 75.11% in the restaurant domain.

Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.

A novel category detection of social media reviews in the restaurant industry

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches.

ASPECT CATEGORY POLARITY DETECTION USING MULTI CLASS SUPPORT VECTOR MACHINE WITH LEXICONS BASED FEATURES AND VECTOR BASED FEATURES

Sentiment Analysis (SA) is the classification and interpretation of the opinions or emotions within the text data using text analysis methods. Aspect Level Sentiment Analysis (ALSA) is a type of SA technique which uses different types of algorithms to extract the aspects of the entities from the text data and determining the sentiment of the aspects. ALSA is divided into two subtasks such as Aspect Category Detection (ACD) and Aspect Category Polarity Detection (ACPD). ACD determines the aspect categories discussed in each sentence from a set of review sentences and a given predefined set of aspect categories. Aspect categories need not occur as terms in the sentences and are characteristically coarser than the aspect terms. For every review sentence the aspect categories are provided to identify the polarity of aspect category. The objective of ACPD is to identify the polarity such as negative, positive, neutral or conflict of every aspect category mentioned in each sentence. In this paper, the task aspect category polarity detection is addressed. The three types of features such as linguistic features, Lexicons features and Vector Space features are extracted from the reviews. The classifier is trained with these features. The learned model is used to detect the polarities of the categories that are present in the test review sentences. The combination of proposed features with the existing features improves the accuracy of the system. The accuracy of the proposed system has enhanced by 1.3 % compared with best performing system.

Aspect category detection using statistical and semantic association

AbstractAspect category detection (ACD) is an important subtask of aspect‐based sentiment analysis (ABSA). It is a challenging problem due to subjectivity involved in categorization, as well as the existence of overlapping classes. Among various approaches that have been applied to ACD include rule‐based approaches along with other machine learning approaches, and most of them are statistical in nature. In this article, we have used an association rule‐based approach. To deal with the statistical limitation of association rules, we proposed a hybridized rule‐based approach that combines association rules with the semantic association. For semantic associations, we have used the notion of word‐embeddings. Experiments were performed on SemEval dataset, a standard benchmark dataset for aspect categorization in the restaurant domain. We observed that semantic associations can complement statistical association and improve the accuracy of classification. The proposed method performs better than several state‐of‐the‐art methods.

Aspect Category Detection Using Multi Label Multi Class Support Vector Machine with Semantic and Lexical Features

Aspect Category Detection Using Multi Label Multi Class Support Vector Machine with Semantic and Lexical Features

MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM

Online reviews play an increasingly important role in users' purchase decisions. E-commerce websites provide massive user reviews, but it is hard for individuals to make full use of the information. Therefore, it is an urgent task to classify, analyze and summarize the massive comments. In this paper, a model based on attention mechanism and bi-directional long short-term memory (BLSTM) is used to identify the categories of these review objects for the classification of the reviews. The model first uses BLSTM to train the review in the form of word vectors; then according to the part-of-speech, the output vectors of the BLSTM are given corresponding weights. The weights as prior knowledge can guide the learning of attention mechanism to enhance the classification accuracy; finally, the attention mechanism is used to capture category-related important features which are used for category determination. Experiments on the SemEval data set show that our model outperforms the state-of-the-art methods on aspect category detection.

Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities

Governments these days are demanding better Smart City technologies in order to connect with citizens and understand their demands. For such governments, much needed information exists on social media where members belonging to diverse groups share different interests, post statuses, review and comment on various topics. Aspect extraction from this data can provide a thorough understanding of citizens’ behaviors and choices. Also, categorization of these aspects can better summarize societal concerns regarding political, economic, religious and social issues. Aspect category detection (ACD) from people reviews is one of the major tasks of aspect-based sentiment analysis (ABSA). The success of ABSA is mainly defined by the inexpensive and accurate machine-processable representation of the raw input sentences. Previous approaches rely on cumbersome feature extraction procedures from sentences, which adds its own complexity and inaccuracy in performing ACD tasks. In this paper, we propose an inexpensive and simple method to obtain the most suitable representation of a sentence-vector through different algebraic combinations of a sentence’s word vectors, which will act as an input to any machine learning classifier. We have tested our technique on the restaurant review data provided in SemEval-2015 and SemEval-2016. SemEval is a series of global challenges to evaluate the effectiveness of disambiguation of word sense. Our results showed the highest F1-scores of 76.40% in SemEval-2016 Task 5, and 94.99% in SemEval-2015 Task 12.

Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis

Representing words as real-value vectors and use them as input in deep neural networks is an effective approach in many natural language processing tasks. Currently, some studies use a lower-level representation which is character-based vectors. This paper addresses on how to integrate different representations of input for the problem of aspect-based sentiment analysis. We will propose a joint model of multiple Convolutional Neural Networks (CNNs) in which each individual representation of the input is handled by one CNN. In this work we focus on three kinds of representation including word embeddings from the two methods (Word2Vec and GloVe) and the one-hot character vectors. Our experimental results demonstrate that the proposed model can achieve state-of the-art performance in aspect category detection and aspect sentiment classification tasks.