Search engines are widely used for finding information on the internet. However, there are limitations in the current search approach, such as providing popular but not necessarily relevant results. This research addresses the issue of polysemy in search results by implementing a search function that determines the sentimentality of the retrieved information. The study utilizes a web crawler to collect data from the British Broadcasting Corporation (BBC) news site, and the sentimentality of the news articles is determined using the Sentistrength program. The results demonstrate that the proposed search function improves recall value while accurately retrieving nonpolysemous news. Furthermore, Sentistrength outperforms deep learning and clustering methods in classifying search results. The methodology presented in this article can be applied to analyze the sentimentality and reputation of entities on the internet.

翻译：搜索引擎被广泛用于在互联网上查找信息。然而，当前搜索方法存在局限性，例如提供流行但未必相关的结果。本研究通过实现一种可判定检索信息情感倾向的搜索功能，解决了搜索结果中的歧义性问题。研究利用网络爬虫从英国广播公司（BBC）新闻网站收集数据，并使用情感强度程序判定新闻文章的情感倾向。结果表明，所提出的搜索功能在准确检索非歧义性新闻的同时提高了召回率。此外，情感强度程序在对搜索结果进行分类时优于深度学习和聚类方法。本文提出的方法论可应用于分析互联网上实体的情感倾向和声誉。