Employee comments provide a rich source of insight and add context to quantitative responses. Topics analysis uses Natural Language Processing (NLP), a field of machine learning, to analyze employee comments and identify important themes.
Keyword topics group comments based on matching keywords, while semantic topics group comments based on their meaning. See Semantic topics for comments for more information.
Background
- Actionable and contextual results. Example: An Additional Training topic from the Peer Relationship driver can require different action compared to the same topic from the Accomplishment driver.
- Multiple language translation support.
- Rich insights extracted from the real meaning behind comments. Example: Low priority on common keywords with little or no meaning in answers and questions. Peakon only generates topics where the words have meaning within the context of a driver.
- A comment extract based on a summary of the most representative lines from one or multiple comments to provide you with an overview of the meaning of the topic.
- Highlighted based on actionability, enabling leaders to focus on what's most important to their teams.
Viewing topics
The Topics page is available in the Insights menu for all managers with access to comments. To be eligible for topics, a segment must have more than 200 comments in 1 language across all kinds of question sets over the last 3 months.
Each topic contains comments in 1 language. The language filter on the topics overview page allows you to see topics generated from comments in a specific language. See Reference: Supported languages for keyword topics.
Each displayed topic includes:
- The topic theme: when the same words display in topics, WPEV groups the topics into themes. Example: Personal Development. Each driver can have a maximum of 3 themes. If the analysis generates more than 3 themes in a single driver, these themes display as single topics.
- The topic extract: a paragraph of the sentences that are most indicative of all the comments that make up a topic. Example: I feel like my personal development has stalled in recent months … It feels like the personal development and career path of certain people is favored. The extract gives you insight into a broad conversation at a glance.
- The topic score: The score of each topic is the average of all scores from the comments in that topic. The scores provide an indication of the general sentiment towards that topic.
Peakon highlights certain topics to help focus your attention.
| Category | Description |
|---|---|
|
High scoring |
The topic with the highest average score, informing managers of the aspects of work employees are most satisfied with. |
|
Low scoring |
The topic with the lowest average score, informing managers of the aspects of work employees are least satisfied with. |
|
Most comments |
The topic with the highest number of comments, informing managers of the themes on employees’ minds or of the most frequent words/language within employee comments. |
|
Consistent comments |
The topic made up of comments repeated in different survey rounds over time, informing managers of consistent themes coming through employee comments over time. |
|
Comment spike |
The topic made up of a large number of comments within a short period of time, informing managers of emerging themes from a one-time event or question. |
How it works
Text analysis
The algorithm can uncover both industry-wide employee experience topics and specific team and time-dependent topics.
You don't need to use predefined terms or manually add keywords of your own. Peakon summarizes a topic by combining the most representative sentences from multiple comments to provide you with a meaningful overview.
Peakon calculates the comments topics once the survey round closes at 1:00 AM UTC the next day.
New topics generate after:
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Questions from 2 active core driver questions. Example: A driver from the Engagement question set and a driver from the Company question set.
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200 or more comments across all kinds of question sets over the last 3 months for 1 language.
People leaders further down the hierarchy will have fewer topics than leaders further up the hierarchy, who have access to more survey results and comments.
Older comments (more than 3 months old) can contribute to new, related topics. However, they can't generate new themes.
Peakon will seek to populate the top 50 topics, and then place the topics under the respective driver. You could potentially have some drivers without topics, despite there being more than 300 comments.
Sentiment analysis
Standard sentiment analysis consists of an algorithm that processes text comments and assigns a numerical value of probability of positive or negative. Example: Scores between -1 and +1, with zero being neutral.
This score is more reliable as it comes directly from an employee in relation to a specific driver question that sets a context. As Peakon asks for feedback after every question, our comments are typically short, simple, and focused, and rarely express conflicting sentiment.
Relevant themes
The Peakon algorithm finds topics from short text answers containing the same words but used in different contexts.
Example 1. Answering a growth question (I feel that I'm growing professionally) an employee might comment: I'm definitely growing in a direction that's more interesting to me, thanks to the development plan that my manager created.
Example 2. Answering an open-ended question (If you had a magic wand what’s the one thing you would change about Kinetar?) An employee could comment: I really wish we would provide more line manager training, this would be really helpful.
In example 1, the word manager relates to an employee interaction with the manager. In example 2, it relates to a manager asking for more training.
Using examples 1 and 2, we would find that the word manager is important because it displays twice. However, with no context to act on, we get no interesting actionable information about why the word is important.
Using contextual analysis, the Peakon algorithm makes topics more actionable and interesting by:
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Attaching topics to drivers, which add context to topics. Using Example 1, the algorithm might find development plan as a topic if it's a combination of words used in relation to growth.
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Finding multiword topics: In example 1, the answer included the word combination development plan. When this word combination displays in many answers, Peakon highlights the topic as development plan and not development. From Example 2, the topic could be line manager training, manager training, or training depending on how often the words occur.
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Removing words from the question phrasing. People have a tendency to repeat the words of the question in the answer. In Example 1, the answer includes the word growing, which was also in the question. It isn't useful to find this word as a topic, since we already know that we're asking about growth. Therefore, Peakon automatically removes the words from the question phrasing.
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Reducing the priority on commonly occurring words. The algorithm reduces the priority on words with no specific meaning that display throughout all survey answers. Manager only displays as a topic if it has a specific meaning in a driver context, providing topics specific to the driver. Example: Office or Room for the environment driver or Pay rise for the driver reward. If we didn't down-prioritize commonly occurring words, we would find manager as a topic for all drivers.
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Delivering a topic extract. The algorithm generates a paragraph from the sentences that are most indicative of all the comments that make up a topic. This paragraph gives you an insight into a broad conversation.
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Ensuring that your topics change over time. Peakon also uses any older comments that are also relevant to the topic. However, the topic will depend on comments made in the last 3 months.
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