The large amount of textual information generated daily on the Internet has increased the importance of tools capable of analysing opinions, emotions, and sentiment expressed in digital content. Social media posts, online discussions, news articles, and user reviews contain valuable information that is often difficult to interpret quickly without automated text analysis techniques. This project presents the design and development of a browser extension focused on sentiment analysis directly within the web browsing environment. The developed application allows users to select text from any web page and analyse it using different Natural Language Processing algorithms integrated through the Senpy framework. The extension includes several functionalities related to both analysis and result visualization. These features include algorithm comparison, multiple graphical representations, keyword heatmap visualizations, persistent analysis history, and customizable user settings. The interface has been developed using modern frontend technologies such as React, Type-Script, and TailwindCSS, following a modular approach and aiming to provide an intuitive and user-friendly experience. One of the most relevant aspects of the project is the implementation of explainable visualization mechanisms capable of highlighting emotionally relevant words directly inside the analysed text. This allows users not only to obtain the final sentiment classification, but also to better understand how the analysis has been generated. In addition, the project demonstrates how Natural Language Processing services can be efficiently integrated into browser extensions using modern extension APIs and lightweight architectures based on Docker containers. Overall, the project shows the feasibility of combining modern web technologies and Natural Language Processing techniques to build interactive and explainable sentiment analysis tools integrated into everyday web browsing activities.