360 degree view of cross-domain opinion classification: a survey

Singh, Robert P.; Sachan, Manoj Kumar; Patel, R. B.

doi:10.1007/s10462-020-09884-9

Cited by 27 publications

(10 citation statements)

References 292 publications

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“…This method uses non-contextual word embedding within a deep neural network and performed a transfer of knowledge representation. This approach is one of the first studies leveraging transfer learning and deep learning and is used in other studies for issue classification task [21], [57], [58], and therefore we choose it as a prior approach in our study.…”

Section: B Prior Approachesmentioning

confidence: 99%

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Hadi¹,

Fard²

2021

Preprint

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Context: Mobile app reviews written by users on app stores or social media are significant resources for app developers. Analyzing app reviews have proved to be useful for many areas of software engineering (e.g., requirement engineering, testing). Automatic classification of app reviews requires extensive efforts to manually curate a labeled dataset. When the classification purpose changes (e.g. identifying bugs versus usability issues or sentiment), new datasets should be labeled, which prevents the extensibility of the developed models for new desired classes/tasks in practice. Recent pre-trained neural language models (PTM) are trained on large corpora in an unsupervised manner and have found success in solving similar Natural Language Processing problems. However, the applicability of PTMs is not explored for app review classification.Objective: We investigate the benefits of PTMs for app review classification compared to the existing models, as well as the transferability of PTMs in multiple settings.Method: We empirically study the accuracy and time efficiency of PTMs compared to prior approaches using six datasets from literature. In addition, we investigate the performance of the PTMs trained on app reviews (i.e. domain-specific PTMs). We set up different studies to evaluate PTMs in multiple settings: binary vs. multi-class classification, zero-shot classification (when new labels are introduced to the model), multi-task setting, and classification of reviews from different resources. The datasets are manually labeled app review datasets from Google Play Store, Apple App Store, and Twitter data. In all cases, Micro and Macro Precision, Recall, and F1-scores will be used and we will report the time required for training and prediction with the models.

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Section: B Prior Approachesmentioning

confidence: 99%

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Hadi¹,

Fard²

2021

Preprint

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“…1 Sentiment analysis (also known as opinion mining) is one of the growing subarea of natural language processing (NLP) and it has achieved enormous attention from numerous researchers all around the world in recent decades. 2,3 The main objective of opinion mining is to classify the emotional tendencies (negative, neutral, or positive) of text documents. Supervised learning techniques usually perform well if a sufficient labeled dataset exists for a specific domain (Ibeke et al, 2020) 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Ever growing utilization of online activities, internet uses, and user‐generated content leads us to analyze, load, transform, extract, and mining large‐amount of unstructured and structured data 1 . Sentiment analysis (also known as opinion mining) is one of the growing subarea of natural language processing (NLP) and it has achieved enormous attention from numerous researchers all around the world in recent decades 2,3 . The main objective of opinion mining is to classify the emotional tendencies (negative, neutral, or positive) of text documents.…”

Section: Introductionmentioning

confidence: 99%

Cross‐domain opinion classification via aspect analysis and attention sharing mechanism

Singh

Sachan

Patel

2022

Concurrency and Computation

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The purpose of cross-domain opinion classification is to leverage useful information acquired from the source domain to train a classifier for opinion classification in the target domain, which has a huge amount of unlabeled data. An opinion classifier trained on a specific domain usually acts poorly, when directly employed to another domain.Annotating the data for all the domains is a laborious and costly process. The majority of available approaches are centered on identifying invariant features among domains.Unluckily, they are unable to properly capture the context within the sentences and better utilization of unlabeled data. To properly address this issue, we propose an aspect-based attention model for cross-domain opinion classification. By incorporating knowledge of aspects and sentences, the proposed model provides a transfer mechanism for better-transferring opinions among domains. We introduce two learning networks, first learning network aims to recognize the shared features between domains, while the purpose of the second learning network is to extract the information from the aspects by utilizing shared words as a bridge. We benefit from BERT and bidirectional gated recurrent unit to get a deep understanding and deep level semantic information of the text. Further, the joint attention learning mechanism is performed for these two learning modules so that the aspects and sentences can impact the resulting opinion expression. In addition, we introduce a gradient reversal layer to obtain invariance features. The comprehensive experiments are performed on Amazon multidomain product datasets and show the effectiveness and significance of the proposed model over state-of-the-art techniques.

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“…As a result, consumers from all over the world are sharing their emotions, opinions, evaluations and judgments to a wide ranging audience by connecting themselves to online platforms such as blogs, newsgroups, discussion boards, and social networking sites [4][5][6]. Consequently, the Web consists of huge volumes of publicly available opinion data about different objects, for instance, individuals, government, products, events, organizations, services, education, news [7,8]. The volume of opinion data about different entities (individuals, products, events, organizations, services) is growing rapidly on these platforms due to the accessibility, scalability, and enhanced user participation of Web 2.0.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Online Decision-Making Driven by Opinion Mining

et al. 2021

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With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process.

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360 degree view of cross-domain opinion classification: a survey

Cited by 27 publications

References 292 publications

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Cross‐domain opinion classification via aspect analysis and attention sharing mechanism

Multi-Attribute Online Decision-Making Driven by Opinion Mining

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