Knowledge extraction by integrating emojis with text from online reviews

IF 6.6 2区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Kuoyi Lin, Xiaoyang Kan, Meilian Liu
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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.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>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. 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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. 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引用次数: 0

Abstract

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.

通过整合表情符号和在线评论文本进行知识提取
目的 本研究开发并验证了一种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的创新方法。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着举足轻重的作用,本研究旨在弥补现有情感分析模型的重大缺陷,因为这些模型在很大程度上忽视了表情符号在解读用户偏好和情感方面的贡献。本研究通过构建一个综合模型,协同表情符号和文本传递的情感和语义信息,力求提供对用户偏好更细致入微的理解,从而提高从在线评论中提取知识的准确性和深度。本研究的目标是提供一个强大的框架,通过对消费者行为和情感的知情洞察,更有效、更有同理心地参与数字平台上的用户生成内容,为改善服务提供、产品开发和客户满意度铺平道路。设计/方法/途径本研究采用结构化方法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感。该方法包括四个关键阶段。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奠定基础。其次,作者建立了一个基础情感词典,其中包含了情感词、强化词和否定词,用于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,从而根据这些预定义词汇的存在和组合对文本情感进行量化。第三,开发了表情符号情感字典,以处理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其进行分类,从而实现对评论中情感表达的量化。然后将从表情符号中得出的情感分数与文本分析中得出的分数进行整合。这种整合考虑了基于文本和表情符号的情感的权重,从而计算出综合属性情感分数,反映出对用户情感和偏好的细微理解。最后,作者进行了一项实证研究,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感的能力,验证了所提出的方法在从在线评论中挖掘用户偏好方面的有效性。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见解的可靠性和准确性。本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,同时捕捉用户通过文本和表情符号表达的显性和隐性情感。通过整合这些元素,本研究旨在提供对用户偏好的全面理解,从而帮助企业和研究人员提高对消费者的洞察力并做出战略决策。 研究结果应用所提出的方法将在线评论中的表情符号与文本整合在一起,得出了重要的研究结果,强调了从用户生成的内容中提取真实的用户知识以获得洞察力的可行性和价值。分析成功地捕捉到了消费者的偏好,这有助于为服务决策提供信息并推动创新。这一成果在很大程度上归功于开发和使用了一个全面的情感-情绪字典,该字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。通过实施将文本情感分析与表情符号情感分析巧妙结合的情感计算模型,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。该模型有效地描述了在线用户的情感知识,并提供了对其情感和偏好的细微理解。所提取的情感知识不仅可以量化,而且具有丰富的语境,可以为消费者的行为和态度提供更深入的洞察。此外,我们还进行了案例分析,严格检验了所提模型在真实世界场景中的有效性。实际检验结果表明,该模型不仅能够准确提取和分析用户偏好,还能适应不同的环境和产品类别。
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来源期刊
CiteScore
13.70
自引率
15.70%
发文量
99
期刊介绍: Knowledge Management covers all the key issues in its field including: ■Developing an appropriate culture and communication strategy ■Integrating learning and knowledge infrastructure ■Knowledge management and the learning organization ■Information organization and retrieval technologies for improving the quality of knowledge ■Linking knowledge management to performance initiatives ■Retaining knowledge - human and intellectual capital ■Using information technology to develop knowledge management ■Knowledge management and innovation ■Measuring the value of knowledge already within an organization ■What lies beyond knowledge management?
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