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Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly determine the orientation of aspect related sentiment bearing word and overlook the aspect-context. Therefore, this paper aims to propose an aspect-context aware sentiment classification of OCR for deeper and more accurate insights.

In the proposed methodology, first, aspect descriptions and sentiment bearing words are extracted. Then, the skip-gram model is used to extract the first set of features to capture contextual information. For the second category of features, cosine similarity is used between a pre-defined seed word list and aspects, to capture aspect context sensitive sentiments. The third set of features includes weighted word vectors using term frequency-inverse document frequency. After concatenating features, ensemble classifier is used using three base classifiers.

Experimental results on two real-world data sets with variable lengths, acquired from Amazon.com and TripAdvisor.com, show that the advised ensemble approach significantly outperforms sentiment classification accuracy of state-of-the-art and baseline methods.

This method is capable of capturing the correct sentiment of ambiguous words and other special words by extracting aspect-context using word vector similarity instead of expensive lexical resources, and hence, shows superior performance in terms of accuracy as compared to other methods.

Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC.

A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words.

Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains.

Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach.

This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

本研究使用集成的四层神经网络构建面向方面的情感分类模型：BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews.

Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values.

A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof.

The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period.

This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability.

Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.

The purpose of this study is to explore the impact of other customer's opinion on a service firm and its alliance on the evaluation of the airline by the focal customer by integrating qualitative and quantitative user-generated content. The study also explores the relative importance of core and peripheral attributes in consumer evaluations.

A text mining and natural language processing-based approach was followed to extract insights from the qualitative part of 18,457 consumer reviews, which were later analyzed along with the quantitative information obtained from the reviews using linear regression and logistic regression methods.

The authors found that customer satisfaction and recommendation behavior is formed by own and others' opinion about the airline and alliance. The relative importance of the core and peripheral attributes depends on the psychological distance from the evaluation of the attribute.

The theoretical contribution and managerial implications have been discussed in detail.

It helps in review management strategy, service design strategy and the alliance and partnership strategies of the airlines.

This is the first paper that explores the impact attribute-level evaluations found in prior reviews on the future reviews of customers. It also explores the effect of prior reviews in the context of a service business and its alliances.

The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion.

The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains.

The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques.

The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks.

The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

Corporate social responsibility (CSR) is significant in the financial market. Despite plenty of existing research on CSR, few studies have quantified the fine-grained aspects of CSR and examined how diverse CSR aspects are associated with firms' trade credit. Based on the released CSR reports, this paper strives to measure the CSR fulfillment of firms and examine the relationships between CSR and trade credit in terms of textual features presented in these reports.

This research proposes a natural language processing-based framework to extract the overall readability and the sentiment of fine-grained aspects from CSR reports, which can signal the performance of firms' CSR in diverse aspects. Furthermore, this paper explores how the textual features are associated with trade credit through partial dependence plots (PDPs), and PDPs can generate both linear and nonlinear relationships.

The study’s results reveal that the overall readability of the reports is positively associated with trade credit, while the performance of the fine-grained CSR aspects mentioned in the CSR reports matters differently. The performance of the environment has a positive impact on trade credit; the performance of creditors, suppliers and information disclosure, shows a U-shaped influence on trade credit; while the performance of the government and customers is negatively associated with trade credit.

This study expands the scope of research on CSR and trade credit by investigating fine-grained aspects covered in CSR reports. It also offers some managerial implications in the allocation of CSR resources and the presentation of CSR reports.

Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework – PRODWeakFinder – to expect to detect product weaknesses through sentiment analysis in an effective way.

PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations.

The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy.

Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework.

The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies.

A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare.

An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.