As you’ve already seen, data is analyzed in Keatext by breaking down long-form written feedback into Comments, identifying a Topic and an Opinion for each, and then bucketing the Comment into one of four sentiments: Praises, Problems, Questions or Suggestions.

We also have "Comments without opinions", previously known as Mentions. You are probably wondering what it’s good for.

This consists of Comments containing a Topic with no associated Opinion. For example, the Comment “Here are some tips where to go to eat” contains a Topic (tips) but doesn’t express an Opinion about the tips — they aren’t described as “good tips” or “juicy tips.”

Single-word responses containing only a Topic are also included under "Comments without opinions". For example if you had a survey asking for your three favourite types of food and someone responded, "sushi, pizza, burgers", the words would be picked up under "comments without opinions"

Comments without opinions are useful if you need to know how often something is mentioned in the feedback. For example, you may have a survey on hair care products where one of the questions is “What is your favourite hairspray and why?” Some respondents may ignore the “why” part of the question and just list their favourite hairspray — a one-word response. Including "Comments without opinions" in your analysis allows you to see how many times that particular hairspray has been indicated by the respondents, whether or not they say why.

Another example is help desk tickets. Customers are often prompted to enter their reason for seeking help — it could be “wifi connectivity” or “billing.” They may often type in one-word responses rather than a full sentence. Using "Comments without opinions" in this case is a good way to easily identify which issues have the highest frequency and focus on those.

Keep in mind that if tallying frequency is no of interest to you, you can alway choose to deselect this category and conduct a complete analysis without it.