A.M. Abirami and A. Askarunisa
The purpose of this paper is to develop a systematic approach to extract users’ feelings and emotions about their experiences in hospitals from online reviews and rank the places…
Abstract
Purpose
The purpose of this paper is to develop a systematic approach to extract users’ feelings and emotions about their experiences in hospitals from online reviews and rank the places using multi-criteria decision making (MCDM) techniques based on the aggregated sentiment score.
Design/methodology/approach
The proposed model used a linguistic approach to extract the sentiment words from the free text. It used term frequency-inverse document frequency values to represent features of various places in bag-of-words format. Sentiment dictionary is used to calculate senti-scores. It used different MCDM techniques like simple additive weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods for ranking hospitals based on their aggregated senti-score.
Findings
Statistical correlation analysis between the rankings of places reveals that the TOPSIS method is the most suitable ranking technique among other MCDM techniques. By improving the senti-score, one can bring their enterprise to the top position.
Research limitations/implications
Data set is collected from different websites like Twitter, Facebook, etc., for various services/features. Moderate amount of reviews are collected for each place. But not all users give their views on the social media websites. It would be essential to collect responses from all the customers who avail different services at different places.
Practical implications
The sentiment analysis model proposed in this paper enables B2C and C2C commerce. Business may take suitable measures to overcome their issues/problems raised by the consumer. Consumers can share and educate other consumers about their experiences.
Social implications
The development of internet has strong influence in all types of industries like healthcare. The availability of internet has changed the way of accessing the information and sharing their experience with others. This paper recognizes the use and impact of social media on the healthcare industry by analyzing the users’ feelings expressed in the form of free text. A suitable decision-making technique is applied to rank the places, which enables the users to plan their treatment place in a better way.
Originality/value
The paper develops a novel approach by applying the TOPSIS method to rank the different alternative places of the healthcare industry by using the senti-score derived from the users’ feelings, emotions and experiences expressed in the form of free text.
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Marlon Santiago Viñán-Ludeña and Luis M. de Campos
The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative…
Abstract
Purpose
The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.
Design/methodology/approach
The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.
Findings
The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.
Research limitations/implications
The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.
Practical implications
The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.
Originality/value
This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.
研究目的
这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体(或地点)和用户的感知(或方面)。
研究设计/方法/途径
我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达(西班牙)的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。
研究发现
测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。
研究局限性
我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。
实践意义
该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。
研究原创性
本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT(深度学习模型)训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性(消极性)的原因。
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Social media is characterized by its volume, its speed of generation and its easy and open access; all this making it an important source of information that provides valuable…
Abstract
Purpose
Social media is characterized by its volume, its speed of generation and its easy and open access; all this making it an important source of information that provides valuable insights. Content characteristics such as valence and emotions play an important role in the diffusion of information; in fact, emotions can shape virality of topics in social media. The purpose of this research is to fill the gap in event detection applied on online content by incorporating sentiment, more specifically strong sentiment, as main attribute in identifying relevant content.
Design/methodology/approach
The study proposes a methodology based on strong sentiment classification using machine learning and an advanced scoring technique.
Findings
The results show the following key findings: the proposed methodology is able to automatically capture trending topics and achieve better classification compared to state-of-the-art topic detection algorithms. In addition, the methodology is not context specific; it is able to successfully identify important events from various datasets within the context of politics, rallies, various news and real tragedies.
Originality/value
This study fills the gap of topic detection applied on online content by building on the assumption that important events trigger strong sentiment among the society. In addition, classic topic detection algorithms require tuning in terms of number of topics to search for. This methodology involves scoring the posts and, thus, does not require limiting the number topics; it also allows ordering the topics by relevance based on the value of the score.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2019-0373
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Gülçin Büyüközkan and Öykü Ilıcak
SWOT (strengths, weaknesses, opportunities, threats) analysis is a powerful approach for evaluating the strengths and weaknesses of an organization with an internal perspective…
Abstract
Purpose
SWOT (strengths, weaknesses, opportunities, threats) analysis is a powerful approach for evaluating the strengths and weaknesses of an organization with an internal perspective. The approach also takes into account the opportunities and the threats from an external point of view. These features make SWOT a commonly used approach in strategic management. The purpose of this paper is to propose an integrated SWOT analysis with multiple preference relations technique, to show the application of the proposed methodology, to prioritize the strategic factors and to present alternative strategies for ABC, a case company, which is targeting to use social media more effectively.
Design/methodology/approach
In this study, expert opinions are used to identify SWOT factors of ABC on social media. The obtained findings are evaluated and each factor is prioritized by means of the multiple preference relations technique.
Findings
The proposed evaluation model has four main groups, namely, strengths, weaknesses, opportunities, threats, under which 17 factors are identified. As a result of the evaluations, “O2: Opportunity to contact a large number of users simultaneously at affordable cost” has the highest importance level among other factors. Alternative strategies are developed based on the obtained results.
Originality/value
Decision-makers who have different backgrounds or ideas can state their preferences in different formats. Multiple preference relations technique is used to combine different assessments. SWOT analysis with multiple preference relations technique with a group decision-making perspective is proposed. This is the first time the method is used in the social media-related literature. With this study, the most appropriate social media strategic factors are selected for ABC and alternative strategies are determined based on the results.
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Weihua Deng, Ming Yi and Yingying Lu
The helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful…
Abstract
Purpose
The helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.
Design/methodology/approach
A dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.
Findings
The results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.
Practical implications
This paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.
Originality/value
This study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.
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Emna Ben-Abdallah, Khouloud Boukadi, Mohamed Hammami and Mohamed Hedi Karray
The purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.
Abstract
Purpose
The purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.
Design/methodology/approach
propose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.
Findings
The findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.
Research limitations/implications
Limited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.
Practical implications
The SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.
Originality/value
The SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.
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Huiying Gao, Shan Lu and Xiaojin Kou
The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing…
Abstract
Purpose
The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey.
Design/methodology/approach
This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established.
Findings
The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment.
Research limitations/implications
The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality.
Originality/value
This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.