Five-Dimensional Sentiment Analysis of Corpora, Documents and Words

Sentiment analysis has become a widely used approach to assess the emotional content of written documents such as customer feedback. In positive psychology research, the typical one-dimensional analysis framework has been extended to include five dimensions. This five-dimensional model, PERMA, enables a fine-grained analysis of written texts. We propose an approach in which this model, statistical analysis and the self-organizing map are used. We analyze corpora from various genres. A hybrid methodology that uses the self-organizing maps algorithm and human judgment is suggested for expanding the PERMA lexicon. This vocabulary expansion can be useful for English but it is potentially even more crucial in the case of other languages for which the lexicon is not readily available. The challenges and solutions related to the text mining of texts written in a morphologically complex language such as Finnish are also considered.Peer reviewe

Optimal measurement conditions for EEG/MEG source analysis

Electromagnetic source analysis yields estimates of the sources of the Electro- and/or MagnetoEncephaloGram (EEG/MEG) and thus generates a functional description of the human brain. The standard errors of the source estimates are influenced by the number and position of EEG/MEG is measured. Therefore, optimal design theory is applied to determine the required number and position of sensors, the required number of samples, and the required number of trials. To that end, the Fedorov exchange algorithm is extended to incorporate multi-response models. A simulation study and an empirical study on visual evoked potentials indicate that the proposed method is fast and reliable, and improves source precision considerably