Models and tools for value systems analysis in collaborative environments

Dissertation to obtain the degree of Doctor in Electrical and Computer Engineering, specialization of Collaborative Networks

Positive solutions to find a job: Cultural mismatches between companies and employees

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

Improving retail banking staff incentive systems

This Work Project seeks to analyze the viability, utility and best way of implementing mechanisms of double accounting and of insertion of low (or null) sales objectives in an incentives program. The main findings are that both processes are possible and to a certain extent advisable, although in very specific ways and with some limitations. Double accounting processes are especially effective between different segments and networks and should have a greater impact in the first evaluation periods of each case and the null objectives, albeit usable, are recommended to be always substituted by positive objectives, even if quite small. Moreover, it is concluded that the formal structure of the incentives program influences significantly these concepts, namely concerning the duration of the evaluation periods and the interaction of the objectives of different entities for both the vertical (hierarchic) and horizontal (individual and collective) levels.