With the proliferation of digital content and the need for efficient information retrieval, this study's insights can be applied to various domains, including news services, e-commerce, and digital marketing, to provide users with more meaningful and tailored experiences. The study addresses the common problem of polysemy in search engines, where the same keyword may have multiple meanings. It proposes a solution to this issue by embedding a smart search function into the search engine, which can differentiate between different meanings based on sentiment. The study leverages sentiment analysis, a powerful natural language processing (NLP) technique, to classify and categorize news articles based on their emotional tone. This can provide more insightful and nuanced search results. The article reports an impressive accuracy rate of 85% for the proposed smart search function, which outperforms conventional search engines. This indicates the effectiveness of the sentiment-based approach. The research explores multiple sentiment analysis models, including Sentistrength and Valence Aware Dictionary for Sentiment Reasoning (VADER), to determine the best-performing approach. The findings can be applied to enhance search engines, making them more capable of understanding the context and intent behind users 'queries. This can lead to better search results that are more aligned with what users are looking for. The proposed smart search function can improve the user experience by reducing the need to sift through irrelevant search results. This is particularly important in an age where information overload is common.

翻译：随着数字内容的激增及高效信息检索需求的增长，本研究的见解可应用于新闻服务、电子商务及数字营销等多个领域，旨在为用户提供更具意义和个性化的体验。研究聚焦搜索引擎中常见的多义词问题，即同一关键词可能对应多种含义。通过将智能搜索功能嵌入搜索引擎，基于情感特征区分不同语义，提出了解决该问题的方案。研究利用情感分析这一强大的自然语言处理（NLP）技术，根据情感基调对新闻文章进行分类与归纳，从而提供更具洞察力和细粒度的搜索结果。实验表明，该智能搜索功能实现了85%的显著准确率，优于传统搜索引擎，验证了基于情感方法的效果。为确定最优模型，研究比较了Sentistrength与VADER（Valence Aware Dictionary for Sentiment Reasoning）等多种情感分析模型。研究成果可应用于搜索引擎优化，增强系统对用户查询语境及意图的理解能力，使搜索结果更精准契合用户需求。该智能搜索功能通过减少无关结果的筛选需时，有效改善用户体验，在信息过载日益普遍的当代尤为关键。