This project explores the possibilities in detecting changes in opinion over time. For this purpose, different techniques and algorithms in opinion mining have been studied and used as a theoretic foundation when developing strategies towards detecting changes in opinions.Different approaches to a system that detects and visualises changes in opinions have been proposed. These approaches include using machine learning techniques like the naiveBayes algorithm and opinion mining techniques based on SentiWordNet. Additionally,feature extraction techniques and the impact of burst detection have been studied.During this project, experiments have been carried out in order to test some of the techniques and algorithms. A data set containing hotel reviews and a prototype have beenbuilt for this purpose, allowing easy support for testing and validation. Results found high accuracy in opinion mining with the lexicon SentiWordNet, and the prototype can detect hotel features and possible reasons for changes in opinion. It can also show "good" and "bad" geographical areas based on hotel reviews.For commercial use, the prototype can help analyse the massive amount of hotel informa-tion published each day by customers, and can help hotel managers analyse their products. It can also be used as a more advanced hotel search engine where users can find extra information in a map user interface.