  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.
