Purpose

This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role emojis play in enhancing the expressiveness and emotional depth of digital communication, this study aims to address the significant gap in existing sentiment analysis models, which have largely overlooked the contribution of emojis in interpreting user preferences and sentiments. By constructing a comprehensive model that synergizes emotional and semantic information conveyed through emojis and text, this study seeks to provide a more nuanced understanding of user preferences, thereby enhancing the accuracy and depth of knowledge extraction from online reviews. The goal is to offer a robust framework that enables more effective and empathetic engagement with user-generated content on digital platforms, paving the way for improved service delivery, product development and customer satisfaction through informed insights into consumer behavior and sentiments.

Design/methodology/approach

This study uses a structured methodology to integrate and analyze text and emojis from online reviews for effective knowledge extraction, focusing on user preferences and sentiments. This methodology consists of four key stages. First, this study leverages high-frequency noun analysis to identify and extract product attributes mentioned in online user reviews. By focusing on nouns that appear frequently, the authors can systematically discern the primary features or aspects of products that users discuss, thereby providing a foundation for a more detailed sentiment and preference analysis. Second, a foundational sentiment dictionary is established that incorporates sentiment-bearing words, intensifiers and negation terms to analyze the textual part of the reviews. This dictionary is used to assign sentiment scores to phrases and sentences within reviews, allowing the quantification of textual sentiments based on the presence and combination of these predefined lexical items. Third, an emoticon sentiment dictionary is developed to address the emotional content conveyed through emojis. This dictionary categorizes emojis based on their associated sentiments, thus enabling the quantification of emotional expressions in reviews. The sentiment scores derived from the emojis are then integrated with those from the textual analysis. This integration considers the weights of text- and emoji-based emotions to compute a comprehensive attribute sentiment score that reflects a nuanced understanding of user sentiments and preferences. Finally, the authors conduct an empirical study to validate the effectiveness of the proposed methodology in mining user preferences from online reviews by applying the approach to a data set of online reviews and evaluating its ability to accurately identify product attributes and user sentiments. The validation process assessed the reliability and accuracy of the methodology in extracting meaningful insights from the complex interplay between text and emojis. This study offers a holistic and nuanced framework for knowledge extraction from online reviews, capturing both explicit and implicit sentiments expressed by users through text and emojis. By integrating these elements, this study seeks to provide a comprehensive understanding of user preferences, contributing to improved consumer insight and strategic decision-making for businesses and researchers.

Findings

The application of the proposed methodology for integrating emojis with text in online reviews yields significant findings that underscore the feasibility and value of extracting realistic user knowledge to gain insights from user-generated content. The analysis successfully captured consumer preferences, which are instrumental in informing service decisions and driving innovation. This achievement is largely attributed to the development and utilization of a comprehensive emotion-sentiment dictionary tailored to interpret the complex interplay between textual and emoji-based expressions in online reviews. By implementing a sentiment calculation model that intricately combines textual sentiment analysis with emoji sentiment analysis, this study was able to accurately determine the final attribute emotion for various product features discussed in the reviews. This model effectively characterized the emotional knowledge of online users and provided a nuanced understanding of their sentiments and preferences. The emotional knowledge extracted is not only quantifiable but also rich in context, offering deeper insights into consumer behavior and attitudes. Furthermore, a case analysis is conducted to rigorously test the validity of the proposed model in a real-world scenario. This practical examination revealed that the model is not only capable of accurately extracting and analyzing user preferences but is also adaptable to different contexts and product categories. The case analysis highlights the robustness and flexibility of the model, demonstrating its potential to enhance the precision of knowledge extraction processes significantly. Overall, the results confirm the effectiveness of the proposed approach in integrating text and emojis for comprehensive knowledge extraction from online reviews. The findings validate the model’s capability to offer actionable insights into consumer preferences, thereby supporting more informed and strategic decision-making by businesses. This study contributes to the broader field of sentiment analysis by showcasing the untapped potential of emojis as valuable indicators of user sentiments, opening new avenues for research and applications in digital marketing and consumer behavior analysis.

Originality/value

This study introduces a pioneering approach to extract knowledge from Web user interactions, notably through the integration of online reviews that incorporate both textual content and emoticons. This innovative methodology stands out because it holistically considers the dual channels of communication, text and emojis, to comprehensively mine Web user preferences. The key contribution of this study lies in its novel insights into the extraction of consumer preferences, advancing beyond traditional text-based analysis to embrace nuanced expressions conveyed through emoticons. The originality of this study is underpinned by its acknowledgment of emoticons as a significant and untapped source of sentiment and preference indicators in online reviews. By effectively merging emoticon analysis and emoji emotion scoring with textual sentiment analysis, this study enriches the understanding of Web user preferences and enhances the accuracy and depth of consumer preference insights. This dual-analysis approach represents a significant leap forward in sentiment analysis, setting a new standard for how digital communication can be leveraged to derive meaningful insights into consumer behavior. Furthermore, the results have practical implications to businesses and marketers. The insights gained from this integrated analytical approach offer a more granular and emotionally nuanced view of customer feedback, which can inform more effective marketing strategies, product development and customer service practices. By pioneering this comprehensive method of knowledge extraction, this study paves the way for future research and practice to interpret and respond more accurately to the complex landscape of online consumer expressions. This study’s originality and value lie in its innovative method of capturing and analyzing the rich tapestry of Web user communication, offering a ground-breaking perspective on consumer preference extraction that promises to enhance both academic research and practical applications in the digital era.

中文翻译：

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通过将表情符号与在线评论中的文本相结合来提取知识

 目的

这项研究开发并验证了一种创新方法，通过整合文本内容和表情符号从在线用户评论中提取知识。认识到表情符号在增强数字通信的表现力和情感深度方面发挥的关键作用，本研究旨在解决现有情感分析模型中的重大差距，这些模型在很大程度上忽视了表情符号在解释用户偏好和情感方面的贡献。通过构建一个综合模型，协同通过表情符号和文本传达的情感和语义信息，本研究旨在提供对用户偏好的更细致的理解，从而提高从在线评论中提取知识的准确性和深度。目标是提供一个强大的框架，使用户能够更有效、更富有同理心地参与数字平台上的用户生成内容，通过对消费者行为和情绪的深入洞察，为改善服务交付、产品开发和客户满意度铺平道路。

设计/方法论/途径

本研究使用结构化方法来整合和分析在线评论中的文本和表情符号，以有效提取知识，重点关注用户偏好和情绪。该方法由四个关键阶段组成。首先，本研究利用高频名词分析来识别和提取在线用户评论中提到的产品属性。通过关注频繁出现的名词，作者可以系统地辨别用户讨论的产品的主要特征或方面，从而为更详细的情感和偏好分析提供基础。其次，建立了一个基础情感词典，其中包含情感词、强化词和否定词来分析评论的文本部分。该词典用于为评论中的短语和句子分配情感分数，从而允许根据这些预定义词汇项的存在和组合来量化文本情感。第三，开发了表情符号情感词典来解决通过表情符号传达的情感内容。该词典根据表情符号的相关情感对表情符号进行分类，从而能够量化评论中的情感表达。然后将表情符号得出的情绪分数与文本分析得出的情绪分数相结合。这种集成考虑了基于文本和表情符号的情感权重来计算综合属性情感分数，该分数反映了对用户情感和偏好的细致入微的理解。 最后，作者进行了实证研究，通过将该方法应用于在线评论数据集并评估其准确识别产品属性和用户情绪的能力，验证了所提出的方法从在线评论中挖掘用户偏好的有效性。验证过程评估了该方法从文本和表情符号之间复杂的相互作用中提取有意义的见解的可靠性和准确性。这项研究为从在线评论中提取知识提供了一个全面而细致的框架，捕捉用户通过文本和表情符号表达的明确和隐含的情感。通过整合这些要素，本研究旨在提供对用户偏好的全面了解，有助于提高消费者洞察力和企业和研究人员的战略决策。

 发现

应用所提出的将表情符号与在线评论中的文本集成的方法产生了重要的发现，强调了提取现实的用户知识以从用户生成的内容中获取见解的可行性和价值。该分析成功捕捉了消费者的偏好，这有助于为服务决策提供信息并推动创新。这一成就很大程度上归功于综合情感词典的开发和利用，该词典专门用于解释在线评论中文本和基于表情符号的表达之间复杂的相互作用。通过实现将文本情感分析与表情符号情感分析错综复杂地结合起来的情感计算模型，本研究能够准确确定评论中讨论的各种产品功能的最终属性情感。该模型有效地表征了在线用户的情感知识，并提供了对其情感和偏好的细致入微的理解。提取的情感知识不仅是可量化的，而且具有丰富的背景，可以更深入地洞察消费者的行为和态度。此外，还进行了案例分析，以严格测试所提出模型在现实场景中的有效性。此次实践检验表明，该模型不仅能够准确提取和分析用户偏好，而且能够适应不同的环境和产品类别。案例分析强调了该模型的稳健性和灵活性，证明了其显着提高知识提取过程精度的潜力。 总体而言，结果证实了所提出的方法在整合文本和表情符号以从在线评论中全面提取知识方面的有效性。研究结果验证了该模型能够提供有关消费者偏好的可行见解，从而支持企业做出更明智的战略决策。这项研究通过展示表情符号作为用户情绪的宝贵指标的未开发潜力，为数字营销和消费者行为分析的研究和应用开辟了新的途径，为更广泛的情绪分析领域做出了贡献。

 原创性/价值

这项研究引入了一种从网络用户交互中提取知识的开创性方法，特别是通过整合包含文本内容和表情符号的在线评论。这种创新方法之所以脱颖而出，是因为它全面考虑了沟通、文本和表情符号的双重渠道，以全面挖掘网络用户的偏好。这项研究的主要贡献在于其对提取消费者偏好的新颖见解，超越了传统的基于文本的分析，采用了通过表情符号传达的细致入微的表达方式。这项研究的原创性在于它承认表情符号是在线评论中情绪和偏好指标的重要且尚未开发的来源。通过有效地将表情符号分析和表情符号情感评分与文本情感分析相结合，本研究丰富了对网络用户偏好的理解，并提高了消费者偏好洞察的准确性和深度。这种双重分析方法代表了情感分析的重大飞跃，为如何利用数字通信获得对消费者行为的有意义的洞察设定了新标准。此外，研究结果对企业和营销人员具有实际意义。从这种综合分析方法中获得的见解可以提供更细致、情感上更细致的客户反馈视图，从而为更有效的营销策略、产品开发和客户服务实践提供信息。通过开创这种综合知识提取方法，本研究为未来的研究和实践铺平了道路，以更准确地解释和响应在线消费者表达的复杂情况。 这项研究的原创性和价值在于其创新的方法来捕获和分析丰富的网络用户通信，为消费者偏好提取提供了突破性的视角，有望增强数字时代的学术研究和实际应用。