Aspect Category Detection Research Articles

Incorporating product description to sentiment topic models for improved aspect-based sentiment analysis

Sentiment topic models are used as unsupervised methods to solve the specific problems of the general aspect-based sentiment analysis (ABSA) problem. One of the main problems of the technique is its substandard aspect term extraction, which leads to difficulties in aspect label determination. This paper is focused on improving the aspect term extraction of topic models by incorporating product descriptions to the current state-of-the-art sentiment topic model, Aspect Sentiment Unification Model (ASUM). We present two models that extend from ASUM differently to leverage on the information found in the product description: Seller-aided Aspect-based Sentiment Model (SA-ASM) and Seller-aided Product-based Sentiment Model (SA-PSM). SA-ASM has its topic distribution inside the review while SA-PSM has its topic distribution inside the product description. Based on experiments conducted to reviews of laptops and mobile phones, results show that SA-ASM performs better in micro-level problems such as sentiment classification and aspect assignment and SA-PSM performs better in macro-level problems like aspect category detection. Both models achieve better performances compared to current topic modeling methods for the ABSA problem.

ABSA Toolkit: An Open Source Tool for Aspect Based Sentiment Analysis

With a rapid increase in e-commerce websites, people are often interested in analyzing customer reviews expressing customer sentiments on different features of a product before making purchase decisions. In this paper, we present ABSA (Aspect-Based Sentiment Analysis) Toolkit developed for performing aspect-level sentiment analysis on customer reviews. The system has two main phases: (a) development phase and (b) production phase. The development phase allows a user to train models for performing aspect level sentiment analysis tasks on the target domain. In the production phase, a web application is provided through which an end user can submit reviews to analyze aspect level sentiments. The system is built using state-of-the-art approaches of aspect term extraction, aspect category detection, and aspect polarity identification. To the best of our knowledge, there is no framework publicly available to build aspect-level sentiment analysis application. All the source code of the ABSA toolkit is available on GitHub.

Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

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 -score of 67%. The second method is a supervised variant that outperforms existing methods with an -score of 84%.

Representation Learning for Aspect Category Detection in Online Reviews

User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage hand-crafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.