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

Akhtar, Md. Shad; Gupta, Deepak; Ekbal, Asif; Bhattacharyya, Pushpak

doi:10.1016/j.knosys.2017.03.020

Cited by 156 publications

(63 citation statements)

References 16 publications

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“…Aspect based sentiment analysis Sentiment analysis refers to the process of extracting explicit or implicit polarity of opinions expressed in textual data (e.g., social media including online consumer reviews [1,7]). Sentiment analysis has been used for information seeking and demand addressing needs on the consumer side, whereas for business owners and other stakeholders for operational decision making (e.g., branding, preventive/reversal actions) [5].…”

Section: Related Workmentioning

confidence: 99%

“…For example, Siering et al [6] utilized text statistics and linguistic information to extract a variety of aspects from airline company reviews, then they trained the supervised classifiers to assign sentiments to different aspects, for the purpose of explaining and providing recommendations to (potential) customers. Additionally, Akhtar et al [7] designed an optimizer-based feature selection method to extract aspects terms from texts, then an ensemble machine learning model was trained to classify sentiments toward extracted aspects.…”

Section: Related Workmentioning

confidence: 99%

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Toward multi-label sentiment analysis: a transfer learning based approach

2020

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Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects that are independent of certain sentiments. In this study, we propose a transfer learning based approach tackling the aforementioned shortcomings of existing ABSA methods. Firstly, the proposed approach extends the ABSA methods with multi-label classification capabilities. Secondly, we propose an advanced sentiment analysis method, namely Aspect Enhanced Sentiment Analysis (AESA) to classify text into sentiment classes with consideration of the entity aspects. Thirdly, we extend two state-of-the-art transfer learning models as the analytical vehicles of multi-label ABSA and AESA tasks. We design an experiment that includes data from different domains to extensively evaluate the proposed approach. The empirical results undoubtedly exhibit that the proposed approach outperform all the baseline approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Toward multi-label sentiment analysis: a transfer learning based approach

2020

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“…For example, Zheng, Wang, and Gao (2015) took Chinese online reviews as the research object, selected N-POS-grams (employ word n-grams plus POS n-grams as sentimental features) and N-char-grams as features to characterize the sentiment text. Akhtar et al (2017) presented a cascaded framework of feature selection and classifier ensemble for sentiment analysis, and compact set of features performs better compared to the model that makes use of the complete features.…”

Section: Statistics-based Approachesmentioning

confidence: 99%

Sentiment Analysis of Japanese Tourism Online Reviews

Zhu

Feng

et al. 2019

Journal of Data and Information Science

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Purpose: Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4Travel.Design/methodology/approach: Different statistics-and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency (TF-IDF) method are proposed. We also make seven groups of different rulesbased methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown. Findings:We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism (EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage.Research limitation: Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods. Practical implications:The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews.

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“…We have developed several ensemble approaches [50][51][52][53][54][55] for unsupervised learning tasks. These attempt to improve the robustness of the learning process by combining multiple base learners into a solution, which normally is generally obtained with respect to the average performance of a given individual base learner, leading an effective enabling technique for the joint use of different representations in many pattern recognition systems [56][57][58]. Although these studies have made significant progress, how to measure the importance of multiple matching results without any a priori information and how to harmonize them together is still a challenging task.…”

Section: Weighted Ensemble Of Matching Modelsmentioning

confidence: 99%

Ensemble Learning‐Based Person Re‐identification with Multiple Feature Representations

Yang

Liu

et al. 2018

Complexity

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As an important application in video surveillance, person reidentification enables automatic tracking of a pedestrian through different disjointed camera views. It essentially focuses on extracting or learning feature representations followed by a matching model using a distance metric. In fact, person reidentification is a difficult task because, first, no universal feature representation can perfectly identify the amount of pedestrians in the gallery obtained by a multicamera system. Although different features can be fused into a composite representation, the fusion still does not fully explore the difference, complementarity, and importance between different features. Second, a matching model always has a limited amount of training samples to learn a distance metric for matching probe images against a gallery, which certainly results in an unstable learning process and poor matching result. In this paper, we address the issues of person reidentification by the ensemble theory, which explores the importance of different feature representations, and reconcile several matching models on different feature representations to an optimal one via our proposed weighting scheme. We have carried out the simulation on two well-recognized person reidentification benchmark datasets: VIPeR and ETHZ. The experimental results demonstrate that our approach achieves state-of-the-art performance.

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

Cited by 156 publications

References 16 publications

Toward multi-label sentiment analysis: a transfer learning based approach

Toward multi-label sentiment analysis: a transfer learning based approach

Sentiment Analysis of Japanese Tourism Online Reviews

Ensemble Learning‐Based Person Re‐identification with Multiple Feature Representations

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