Query: aspect category detection

title: Aspect Category Detection in Product Reviews using Contextual Representation
abstract: Aspect category detection (ACD) is one of the challenging subtasks in aspect based sentiment analysis. The goal of this task is to detect implicit or explicit aspect categories from the sentences of user generated reviews. Since annotation over the aspects is timeconsuming, the amount of labeled data is limited for supervised learning. In this paper, we study contextual representations of text segments in the reviews using the BERT model to better extract useful features from them, and train a supervised classifier with a small amount of labeled data for the ACD task. Experimental results obtained on Amazon reviews of six product domains show that our method is effective in some domains.

title: Aspect category detection using statistical and semantic association

title: Aspect Category Detection via Topic Attention Network
abstract: The e commerce has started a new trend in natural language processing through sentiment analysis of user generated reviews. Different consumers have different concerns about various aspects of a specific product or service. Aspect category detection, as a subtask of aspect based sentiment analysis, tackles the problem of categorizing a given review sentence into a set of pre defined aspect categories. In recent years, deep learning approaches have brought revolutionary advances in multiple branches of natural language processing including sentiment analysis. In this paper, we propose a deep neural network method based on attention mechanism to identify different aspect categories of a given review sentence. Our model utilizes several attentions with different topic contexts, enabling it to attend to different parts of a review sentence based on different topics. Experimental results on two datasets in the restaurant domain released by SemEval workshop demonstrates that our approach outperforms existing methods on both datasets. Visualization of the topic attention weights shows the effectiveness of our model in identifying words related to different topics.

title: MNCN: A Multilingual Ngram Based Convolutional Network for Aspect Category Detection in Online Reviews
abstract: 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.

title: Joint Learning for Aspect Category Detection and Sentiment Analysis in Chinese Reviews
abstract: Aspect based sentiment analysis (ABSA) is a valuable task, aiming to predict the sentiment polarities of the given aspects (terms or categories) in review texts. Aspect category sentiment analysis is a sub task of ABSA, which mainly focus on aspect category detection and aspect category polarity identifying. Most of the previous methods employ a pipeline strategy, regarding aspect category detection and category sentiment analysis as two separate tasks, which could not meet the needs of practical application. To address this limitation, we propose an end to end neural network model based on joint learning, which can detect aspect category and identify aspect category polarity simultaneously. We conduct several comparable experiments on a Chinese review dataset and the experimental results show that our proposed model is simpler and more effective than the baseline models.

title: Aspect Category Detection on Indonesian E commerce Mobile Application Review
abstract: E commerce platform has a great influence on the growth of digital economy in Indonesia. This promising sector creates a fierce competition between e commerce platforms. User reviews can be utilized to discover useful information for both developers and users. Developers use them for enhancing their application, while users take them as a consideration for using the application. We perform aspect category detection to retrieve the important aspects in reviews. We gather and analyze any aspect categories related to e commerce and find that new potential aspects can be obtained from mobile application reviews, such as promos and payment. For identifying the aspects, we employ two different approaches, one vs all and single model, for 3748 annotated reviews. In one vs all we compare Naive Bayes, SVM, and the effect on using class weights in SVM, since the distribution in the dataset is imbalanced. Meanwhile, we implement neural networks architecture for single model. We compare CNN to GRU+CNN architecture. GRU+CNN successfully achieves overall best result on both example based and label based performance metrics for multi label task in our dataset. In example based we use Hamming score to calculate the accuracy where GRU+CNN gets 71% and it obtains 78% samples average F1 score for labelbased metric.

title: Supervised Aspect Category Detection of Co occurrence Data using Conditional Random Fields
abstract: Customer's sentiments on products and services are recognized by the internet through consumer reviews. But, to make a purchase decision one cannot identify all the data and make a fair evaluation of a product or resource. Hence, all reviews are summarized using a text processing framework. In this paper, we work on identifying the aspect categories in the review sentences. We have built Conditional Random Field based system for category detection and summarization to achieve the Aspect Based Sentiment Analysis(ABSA) task on Restaurant review dataset. Implicit aspect in product review sentences is a difficult task. In the proposed system explicit as well as implicit aspects are extracted to identify the aspect category.

title: Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co occurrence Data
abstract: Using online consumer reviews as electronic word of mouth to assist purchase decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state of the art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an <inline formula> <tex math notation="LaTeX"$F_{1} /tex math>/inline formula> score of 67% The second method is a supervised variant that outperforms existing methods with an <inline formula> <tex math notation="LaTeX"$F_{1} /tex math>/inline formula> score of 84%

title: LICD: A Language Independent Approach for Aspect Category Detection
abstract: Aspect based sentiment analysis (ABSA) deals with processing and summarizing customer reviews and has been a topic of interest in recent years. Given a set of predefined categories, Aspect Category Detection (ACD) as a subtask of ABSA, aims to assign a subset of these categories to a given review sentence. Thanks to the existence of websites such as Yelp and TripAdvisor, there exist a huge amount of reviews in several languages, and therefore the need for language independent methods in this task seems necessary. In this paper, we propose Language Independent Category Detector (LICD) a supervised method based on text matching without the need for any language specific tools and hand crafted features for identifying aspect categories. For a given sentence, our proposed method performs ACD based on two hypotheses: First, a category should be assigned to a sentence if there is a high semantic similarity between the sentence and a set of representative words of that category. Second, a category should be assigned to a sentence if sentences with high semantic and structural similarity to that sentence belong to that category. To apply the former hypothesis, we used soft cosine measure, and for the latter, word mover's distance measure is utilized. Using these two measures, for a given sentence we calculate a set of similarity scores as features for a one vs all logistic regression classifier per category. Experimental results on the multilingual SemEval 2016 datasets in the restaurant domain demonstrate that our approach outperforms baseline methods in English, Russian, and Dutch languages, and obtains competitive results with the strong deep neural network based baselines in French, Turkish, and Spanish languages.

title: OACD SA: Online Aspect Category Detection for Sentiment Analysis using Unsupervised Learning
abstract: The web is a vast repository of consumer reviews, but it is impossible to go through all reviews to arrive at a fair evaluation when we need to buy any product or service. A recommender system that can quickly categorize reviews is highly needed. A subtask of this system is to categorize the general aspects and check sentiments in consumer reviews to provide a proper suggestion or recommendation to any user. The proposed Online Aspect Category Detection system for Sentiment Analysis (OACD SA) uses the SemEval 2014 Task 4 as a baseline for 'Aspect detection' and then goes on to use online reviews given by users as text corpus for any product or service. Unsupervised learning method along with association rule mining is used to discover aspect categories from a corpus. It uses a novel spreading algorithm to rate every feature which can then be used for evaluation of the overall rating for that product. The unsupervised learning method returns a F1 score of 68% for aspect detection and an aspect sentiment classification accuracy of 66% on baseline task, thus providing robust aspect categorization with a quick response time for any online product. Therefore, the proposed system can be to improve their product or service quality.