The topics shown in the Temperature Gauge visualization are selected on the basis of a strong negative reaction by you, compared with others, or by others, compared with you. The kinds of topics an individual gets heated about, while others do not, tend to be somewhat narrower in scope and to affect that individual more personally, compared with topics that appear in other visualizations, which show topics that were discussed or caused stress more broadly.

The detection starts with analysis of the language used in emails, but it is a complex process (one that linguists have been working on for years). Some facets of language that Digital Mirror takes into account are:

• Different emotions: anger versus hope, for example. Like colors, emotions can blend into one another: worry can blend into anxiety which can turn to fear. One broad categorization is to distinguish positive emotions (joy, happiness) from negative ones (sorrow, sadness).
• Time scale: is an emotion momentary or does it continue for a long time?
• Intensity: people can experience different degrees of the same emotion: a little worried versus very, very worried.
• Causality: does an event cause an emotion (fear caused by danger) or does an emotion give rise to an action (secrecy stemming from fear)?

Digital Mirror tends to focus on negative emotions, since these are more generally associated with interesting events. (To quote Tolstoy: Happy families are all alike; each unhappy family is unhappy in its own way.)

Parts of the timeline at the top of the visualization may be colored yellow. This means that no data from the indicated period was used in this visualization. This version of Digital Mirror shows only a limited amount of information, so it is quite possible relevant data was found for this time, but is not being shown. (The timeline animation runs at higher speed through the yellow-colored regions.)

Digital Mirror analyzes your email data for topics that appear to generate the most negative language when they are discussed. This is detected by linguistic analysis of the tone of the communications - are there indications that the writer is angry or making derogatory comments, for example? The "hottest" topics are selected for display.

If you are running Digital Mirror on personal data, please note that this version is optimized for analysis of data in a work or other organizational context. For personal data, it is possible that you may find some of the results less interesting or relevant.

Even if that is not the case, people are sometimes puzzled by the results Digital Mirror presents. But these results are firmly based on the data: if you see something unexpected, it is likely to be because your data suggests something different than your perception. We strongly recommend that you look into the data yourself. In many cases, you can do this by clicking on the specific result in the visualization to bring up an evidence panel that shows some examples that led to the conclusion. In our experience, in the vast majority of cases, the data will support the Digital Mirror result. The challenge is to see yourself as Digital Mirror and others see you. If you have tried and still believe the result is wrong, please let us know.

Digital Mirror is designed to create a reasonably wide range of visualizations, not all of which will be applicable to everyone. It is quite likely that, for any individual user, there will not be enough relevant data for every single visualization. This is normal, which is why we provide a sample for you to enjoy!