Abstract: Systems and methods for improving the analysis of results from a user experience study are provided. In some embodiments, study results are converted into text if needed. The text is segmented, and qualifiers and entity pairs are identified within the text (semantic analysis). Sentiment for these pairs is also determined. This information is utilized to generate one or more of different analyses of the study results. This includes, for example, the automatic generation of video and/or text clips from the study results that are of greatest interest to the user. This is performed by scoring certain keywords in the segments, generating the clips based upon the scores, and then filtering using trained machine learning models. Another analysis includes the generation of a semantic matrix with sentiment color coding. Intensity of the color may be defined by the frequency the entity-qualifier pair occurs.

Abstract: Systems and methods for attribute determination in a usability study are provided. The system includes the ability to collect screener questions and response pairs and determine the type of question. The question and response pairs may be processed for topic and entity extractions using machine learning (ML) models. From the collected topics and entities, a dictionary of attributes may be generated and eventually expanded/added to as new information regarding the participant becomes available. This attribute dictionary may take the form of a vector dictionary, in some particular embodiments. In some cases, the type of question being posed may dictate how the response is processed. These include a Boolean style question, a quantitative question, a single response question and a multi-response type question.

Abstract: Systems and methods for selecting participants for a user experience study are provided. In some embodiments the systems and methods first receive at least three features for each participant profile. The participant profile is scored by quantile-based discretization. Next the participants are grouped into clusters using an unsupervised machine learning (ML) clustering algorithm(s) for each participant profile. The clusters are ranked using a number of models. These models are a function of geography and study type. The participant profile is assigned to a single cluster for each model. Participants are sampled from the clusters by their ranking.