In an increasing urbanizing world the need for efficient transportation methods is growing. Ridesharing, sharing a taxi trip with multiple passengers, has been proposed as an effective way to contribute towards solving the traffic problems that arise in city centers. Current solutions to the problem are often more focused on optimality, and a fast algorithm is required to handle large quantities of passengers in real-time applications. We propose an algorithm that combines fast and efficient heuristics with clustering and parallelization techniques that is able to solve large problem instances within several seconds. The small response time of the algorithm is able to provide feedback to the customer rapidly, which makes it very suitable for ridesharing in real-time.
A segmentation of the customers is implemented to speed up the search. This method divides the customers into subareas based on pickup location such that each subproblem contains a similar number of customers. This division also bounds the size of each subproblem to ensure a solution can be found in real-time.
The primary search strategy uses a combination of effective local search heuristics to improve solutions for these subproblems rapidly. A technique reminiscent of variable neighborhood search is also implemented for diversification that can solve smaller instances to a greater degree. The full approach can handle problems containing over 5,000 requests and can effectively manage on-demand ridesharing in Manhattan where the current demand for yellow taxis has tripled.

Consumers share their experiences or opinion about products or brands in various channels nowadays, for example on review websites or social media. Sentiment analysis is used to predict the sentiment of text from consumers about these products or brands in order to understand the tone of customers towards these products or brands. This thesis addresses sentiment analysis in the product domain on sentence level. In this thesis three data types are used which are collected by Unilever, review data which is text that contains the opinion of a customer towards a specific product. Social data, which can be tweets, Facebook messages, Instagram messages etc. and phone data which is a summary of a phone call of a customer about a specific product.

When conducting sentiment analysis one solution is to extract features from the data which can be given to a machine learning algorithm together with sentiment labels given by human annotators. The machine learning algorithm will generate a classifier which can predict a label for sentences.
In sentiment analysis literature it is often not clear why certain features are chosen or for which data type certain features will work well. In this research we compare the differences when using several feature sets for the different data types.

We propose three feature sets for review data and three feature sets for social data. We focus on two aspects, comparing the different feature sets and comparing the data types. In our results we do not find significant differences in performance between the feature sets. The results suggest there might be feature sets which can improve sentiment analysis specifically for the data type, but a general feature set with standard features can be comparable to that result.

LeQuest develops interactive e-training modules to improve the competence regarding medical technology of medical professionals. The medical technology is analysed by LeQuest to develop training modules, but the analysis process and writing associated information can be performed more efficiently. This would reduce the required time and resources which could be invested in additional trainings and quality improvements. In the end, this will lead to an improvement regarding the patient's safety in health institutions. This work empirically evaluated the Spoofax Workbench by conducting an industrial case-study which consists of the design, implementation and evaluation of a domain-specific language (DSL). The LeQuest DSL is used as a tool for transforming the current analysis process into a more formalized process which does allow for objective observations, measurements and quantifiable information. Although the LeQuest DSL is not integrated in the current work-flow yet, the evaluation has shown that it is expected that the overall quality and efficiency of the analysis process will increase after the introduction of the DSL.