The quantity of user-generated content on the Web is constantly growing at a fast pace.A great share of this content is made of opinions and reviews on products and services.This electronic word-of-mouth is also an important factor in decisions about purchasing these products or services. Users tend to trust other users, especially if they can compare themselves to those who wrote the reviews, or, in other words, they are condent to share some characteristics. For instance, families will prefer to travel in places that have been recommended by other families. We assume that reviews that contain lived experiences are more valuable, since experiences give to the reviews a more subjective cut, allowing readers to project themselves into the context of the writer.With this hypothesis in mind, in this thesis we aim to identify, extract, and represent reported lived experiences in customer reviews by hybridizing Knowledge Extraction and Natural Language Processing techniques in order to accelerate the decision process. For this, we dene a lived user experience as an event mentioned in a review, where the author is among the participants. This denition considers that mentioned events in the text are the most important elements in lived experiences: all lived experiences are based on events, which on turn are clearly dened in time and space. Therefore, we propose an approach to extract events from user reviews, which constitute the basis of an event-based system to identify and extract lived experiences.For the event extraction approach, we transform user reviews into their semantic representations using machine reading techniques. We perform a deep semantic parsing of reviews, detecting the linguistic frames that capture complex relations expressed in the reviews. The event-based lived experience system is carried out in three steps. The rst step operates an event-based review ltering, which identies reviews that may contain lived experiences. The second step consists of extracting relevant events together with their participants. The last step focuses on representing extracted lived experiences in each review as an event sub-graph.In order to test our hypothesis, we carried out some experiments to verify whether lived experiences can be considered as triggers for the ratings expressed by users. Therefore, we used lived experiences as features in a classication system, comparing with the ratings of the reviews in a dataset extracted and manually annotated from Tripadvisor. The results show that lived experiences are actually correlated with the ratings.In conclusion, this thesis provides some interesting contributions in the eld of opinion mining. First of all, the successful application of machine reading to identify lived experiences. Second, the conrmation that lived experiences are correlated to ratings. Finally, the dataset produced to test our hypothesis constitutes also an important contribution of the thesis.