  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.
