Aspect Term Extraction Research Articles

Aspect Term Extraction Based on MFE-CRF

This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex.

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.

A hybrid unsupervised method for aspect term and opinion target extraction

Aspect term extraction (ATE) and opinion target extraction (OTE) are two important tasks in fine-grained sentiment analysis field. Existing approaches to ATE and OTE are mainly based on rules or machine learning methods. Rule-based methods are usually unsupervised, but they can’t make use of high level features. Although supervised learning approaches usually outperform the rule-based ones, they need a large number of labeled samples to train their models, which are expensive and time-consuming to annotate. In this paper, we propose a hybrid unsupervised method which can combine rules and machine learning methods to address ATE and OTE tasks. First, we use chunk-level linguistic rules to extract nominal phrase chunks and regard them as candidate opinion targets and aspects. Then we propose to filter irrelevant candidates based on domain correlation. Finally, we use these texts with extracted chunks as pseudo labeled data to train a deep gated recurrent unit (GRU) network for aspect term extraction and opinion target extraction. The experiments on benchmark datasets validate the effectiveness of our approach in extracting opinion targets and aspects with minimal manual annotation.

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.

Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis

In this paper we present a cascaded framework of feature selection and classifier ensemble using particle swarm optimization (PSO) for aspect based sentiment analysis. Aspect based sentiment analysis is performed in two steps, viz. aspect term extraction and sentiment classification. The pruned, compact set of features performs better compared to the baseline model that makes use of the complete set of features for aspect term extraction and sentiment classification. We further construct an ensemble based on PSO, and put it in cascade after the feature selection module. We use the features that are identified based on the properties of different classifiers and domains. As base learning algorithms we use three classifiers, namely Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Experiments for aspect term extraction and sentiment analysis on two different kinds of domains show the effectiveness of our proposed approach.

Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multi-layer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves state-of-the-art performances compared with several baselines.

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.

Извлечение аспектных терминов на основе условных случайных полей и векторных представлений слов

There are many sites in the Internet that allow users to share their opinions and write reviews about all kinds of goods and services. These views may be useful not only for other users, but also for companies which want to track their own reputation and to receive timely feedback on their products and services. The most detailed statement of the problem in this area is an aspect-based sentiment analysis, which determines the user attitude not only to the object as a whole, but also to its individual aspects. In this paper we consider the solution of subtask of aspect terms extraction in aspect-based sentiment analysis. A review of research in this area is given. The subtask of aspect terms extraction is considered as a problem of sequence labeling; to solve it we apply the model of conditional random fields (CRF). To create the sequence feature description, we use distributed representations of words derived from neural network models for the Russian language and parts of speech of the analyzed words. The stages of the aspect terms extraction software system are shown. The experiments with the developed software system were carried out on the corpus of labeled reviews of restaurants, created in the International Workshop on Semantic Evaluation (SemEval-2016). We describe the dependence of the quality of aspect terms extraction subtask on various neural network models and the variations of feature descriptions. The best results (F1-measure = 69%) are shown by a version of the system, which takes into account the context and the parts of speech. This paper contains a detailed analysis of errors made by the system, as well as suggestions on possible options for their correction. Finally, future research directions are presented.