Glassdoor recently took a slightly more
qualitative approach, looking not at cut - and - dried stats
like the number of women recruited and promoted, but instead examining where women technologists reported being happiest with their work.
We differentiated between computational
approaches (either based on volume data, such as the number of mentions related to a party or candidate or the occurrence of particular hashtags; or endorsement data, such as the number of Twitter followers, Facebook friends or the number of «
likes» received on Facebook walls), sentiment analysis
approaches, that pay attention to the language and try to attach a
qualitative meaning to the comments (posts, tweets) published by social media users employing automated tools for sentiment analysis (i.e., via natural language processing models or the employment of pre-defined ontological dictionaries), and finally what we call supervised and aggregated sentiment analysis (SASA), that is, techniques that exploit the human codification in their process and focus on the estimation of the aggregated distribution of the opinions, rather than on individual classification of each single text (Ceron et al. 2016).
This might look
like charter - school agreements in the early days — customized contracts that consider «multiple measures» and
qualitative judgments that are better aligned with the mission and
approach of the schools being evaluated (
like the ones you love, Governor «Moonbeam» Brown).