This paper proposes a classification model for opinion mining around airport noise based on techniques such as event detection and sentiment analysis applied on Twitter posts. Tweets are retrieved using the Twitter API either because of location or content.A dataset of preprocessed, with NLP techniques, tweets is manually annotated and then used to train an SVM (Support Vector Machine) classifier in order to extract the relevant ones from the obtained collections. The extracted tweets from the SVM classifier are fed to a lexicon-based classifier to filter out the false relevant and to increase precision. A lexicon-based sentiment classifier is then applied in order to separate positive, negative and neutral tweets. The sentiment classifier uses emoticons, polarity of words with subjective intensity, intensifiers, negation effect with dynamic scope, contrast effect and SWN to detect the sentiment of tweets in a hierarchical manner. The information present in the classified tweets is used for a statistical survey-like study.