{"id":36752,"date":"2018-11-14T10:25:15","date_gmt":"2018-11-14T15:25:15","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/quantifying-employee-feelings\/"},"modified":"2018-11-14T10:25:15","modified_gmt":"2018-11-14T15:25:15","slug":"turning-feelings-into-data-applying-natural-language-processing-to-employee-sentiment","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/turning-feelings-into-data-applying-natural-language-processing-to-employee-sentiment\/","title":{"rendered":"Turning Feelings into Data: Applying Natural Language Processing to Employee Sentiment"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Nowadays, there is little question that deeply understanding and addressing issues of employee engagement is crucial to driving business profit and performance. [1] [2] Unfortunately for companies tackling engagement issues, processing the requisite data poses a serious challenge. While surveys still remain as the most effective method of measuring employee engagement [3], the structure of the data collected poses a challenge, namely in that it is typically <\/span><b>un<\/b><span style=\"font-weight: 400\">structured text.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Survey comments are a treasure trove of data to a people analytics team, providing the depth and color that leaders need to not only flag that a particular engagement measure is low, but more importantly understand <\/span><i><span style=\"font-weight: 400\">why<\/span><\/i><span style=\"font-weight: 400\"> that measure is low. Unfortunately for these practitioners, these (often lengthy) comments are generated in extremely high volumes and are messy with grammar errors, spelling mistakes, and colloquialisms. Only a few companies have the resources to devote the tens (if not hundreds) of hours required to manually read and sort these comments into quantifiable buckets. For those lacking that luxury, turning this feedback into a utilizable dataset proves to be an insurmountable challenge. [4]<\/span><\/p>\n<p><span style=\"font-weight: 400\">This need gap is where companies like Ultimate Perception enter. Perception, formerly known as Kanjoya, pairs an employee survey and performance feedback collection platform with a proprietary natural language processing (NLP) algorithm that makes the process of turning massive amounts of unstructured text into quantifiable insights almost instantaneous. Their machine learning technology focuses on two tasks:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">First, its models analyze each employee-submitted comment and assign a probability of how likely that comment is discussing one of over 70 specific <\/span><b>themes<\/b><span style=\"font-weight: 400\"> (e.g., work-life balance, senior leadership, communication skills, etc.)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Second, the models also determine the probability each of 100 potential <\/span><b>sentiments<\/b><span style=\"font-weight: 400\"> is expressed in the writer\u2019s tone (e.g., confusion, excitement, frustration, etc.). [5]<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">This coupling of two major fields of NLP &#8211; theme clustering and sentiment analysis &#8211; is where Perception\u2019s machine learning technology shines, specifying which themes employees are unhappy about, and which specific percentages and subgroups of employees are unhappy. Not only does this process save time and money: it also reduces risk of an analyst introducing bias in the way they categorize and interpret comments. [6]<\/span><\/p>\n<p><span style=\"font-weight: 400\">Long term, Perception is seeking to build out its NLP software to extend into the myriad of other potential applications across the HR and employee lifecycle (see Exhibit 1). [7] The company already applies its algorithms to analyzing performance reviews, a process they claim can reduce biases of language impacting promotions, especially when combined with feedback recipients\u2019 demographic data like gender and ethnicity. [8] In fact, this combining of NLP with other datasets or even other algorithms holds great potential for Kanjoya, as the company could combine both quantitative and qualitative survey data to develop robust sentiment predictions, or even craft custom individually-tailored engagement action plans for leaders based on the analysis [9]. The technology need not only be applied to volunteered comments; technically, sentiments and themes could be drawn from passively-collected employee-generated text as well (e.g., emails and messenger chats) [10].<\/span><\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-36748\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR-1024x614.jpg\" alt=\"\" width=\"640\" height=\"384\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR-1024x614.jpg 1024w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR-300x180.jpg 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR-768x460.jpg 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR-600x360.jpg 600w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Application-areas-of-NLP-in-HR.jpg 1036w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><\/p>\n<p style=\"text-align: center\"><i><span style=\"font-weight: 400\">Exhibit 1: Potential NLP applications in HR<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400\">With such potential, it\u2019s no wonder Kanjoya was acquired by long-standing HR industry stalwart Ultimate Software, with similar attention devoted to its competitors by the likes of venture capitalists [10] and LinkedIn [11]. So should companies go all-in on investing in an HR NLP vendor? \u00a0Not quite: unlike other applications of NLP technology such as customer sentiment analysis or text data mining, employee-generated data provides its own unique challenges.<\/span><\/p>\n<p><span style=\"font-weight: 400\">To conclude, I pose three potential challenges to consider:<\/span><\/p>\n<ul>\n<li><b>Organizational culture-specific vernacular: <span style=\"font-weight: 400\">Consider cases in which a phrase specific to that company\u2019s culture (e.g., Salesforce\u2019s discussion of \u201cOhana\u201d) is used pervasively in comments. How can Perception\u2019s technology be developed in a scalable way across companies to tag those term clusters and sentiments accurately?<\/span><\/b><\/li>\n<\/ul>\n<ul>\n<li><strong>Tops-down ontology vs. bottoms-up clustering<\/strong>: <span style=\"font-weight: 400\">Related to the above, Perception\u2019s theme clustering is only as accurate as the training data it\u2019s based off of, i.e, historical text data sets and data from other companies. What happens if a new theme trend emerges that isn\u2019t currently captured by the Perception out-of-the-box ontology? Can this issue be addressed given machine learning algorithms for bottoms-up clustering (i.e., self-generated custom clusters) has not caught up yet?<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>Levels of accuracy<\/strong>: <span style=\"font-weight: 400\">While Perception claims accuracy can top 95% for some companies, it acknowledges actual levels may be much lower. [13] For users of the product, though, how can they make an informed decision of how to use the product without knowing how \u201cmuch lower\u201d accuracy rates are? How can they check this accuracy without reverting to their old comment-coding methods? And even if accuracy is at 95%, how does a leader handle potentially explaining to the other 1 out of 20 employees that their comments may have been mistagged, and thus ignored?<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400\">(799 words)<\/span><\/p>\n<p>__<\/p>\n<p><span style=\"font-weight: 400\">[1] Naz Beheshti, \u201cOur Approach to Employee Engagement is Not Working,\u201d <\/span><i><span style=\"font-weight: 400\">Forbes<\/span><\/i><span style=\"font-weight: 400\">, Sep 30, 2018, <\/span><a href=\"https:\/\/www.forbes.com\/sites\/nazbeheshti\/2018\/09\/30\/our-approach-to-employee-engagement-is-not-working\/#2416e7517274\"><span style=\"font-weight: 400\">https:\/\/www.forbes.com\/sites\/nazbeheshti\/2018\/09\/30\/our-approach-to-employee-engagement-is-not-working\/#2416e7517274<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[2] Jacob Morgan, \u201cWhy the Millions We Spend on Employee Engagement Buy Us So Little,\u201d <\/span><i><span style=\"font-weight: 400\">性视界 Business Review<\/span><\/i><span style=\"font-weight: 400\">, Mar 10, 2017, <\/span><a href=\"https:\/\/hbr.org\/2017\/03\/why-the-millions-we-spend-on-employee-engagement-buy-us-so-little\"><span style=\"font-weight: 400\">https:\/\/hbr.org\/2017\/03\/why-the-millions-we-spend-on-employee-engagement-buy-us-so-little<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[3] Scott Judd, O\u2019Rourke, and Grant, \u201cEmployee Surveys Are Still One of the Best Ways to Measure Engagement,\u201d <\/span><i><span style=\"font-weight: 400\">性视界 Business Review, <\/span><\/i><span style=\"font-weight: 400\">Mar 14, 2018. <\/span><a href=\"https:\/\/hbr.org\/2018\/03\/employee-surveys-are-still-one-of-the-best-ways-to-measure-engagement\"><span style=\"font-weight: 400\">https:\/\/hbr.org\/2018\/03\/employee-surveys-are-still-one-of-the-best-ways-to-measure-engagement<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018<\/span><\/p>\n<p><span style=\"font-weight: 400\">[4] Luminoso, \u201cEmployee Feedback and Artificial Intelligence: A guide to using AI to understand employee engagement\u201d (PDF file), downloaded from Luminoso website, <\/span><a href=\"https:\/\/luminoso.com\/writable\/files\/White-Paper-Employee-Feedback-and-AI.pdf\"><span style=\"font-weight: 400\">https:\/\/luminoso.com\/writable\/files\/White-Paper-Employee-Feedback-and-AI.pdf<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[5] Adam Rogers. \u201cHow Unified Employee-Feedback Tools are Revolutionizing HR\u201d. Ultimate Software\u2019s Blog, Feb 7, 2017. <\/span><a href=\"https:\/\/www.ultimatesoftware.com\/blog\/employee-feedback-perception\/\"><span style=\"font-weight: 400\">https:\/\/www.ultimatesoftware.com\/blog\/employee-feedback-perception\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[6] Dan Ring. \u201cMachine learning drives Kanjoya performance review software\u201d. <\/span><i><span style=\"font-weight: 400\">Tech Target<\/span><\/i><span style=\"font-weight: 400\">, Apr 2016. <\/span><a href=\"https:\/\/searchhrsoftware.techtarget.com\/feature\/Machine-learning-drives-Kanjoya-performance-review-software\"><span style=\"font-weight: 400\">https:\/\/searchhrsoftware.techtarget.com\/feature\/Machine-learning-drives-Kanjoya-performance-review-software<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[7] Raja Sengupta, \u201cHow Natural Language Processing can Revolutionize Human Resources\u201d. <\/span><i><span style=\"font-weight: 400\">AIHR Blog &amp; Academy<\/span><\/i><span style=\"font-weight: 400\">. <\/span><a href=\"https:\/\/www.analyticsinhr.com\/blog\/natural-language-processing-revolutionize-human-resources\/\"><span style=\"font-weight: 400\">https:\/\/www.analyticsinhr.com\/blog\/natural-language-processing-revolutionize-human-resources\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[8] Cyrus Sanati, \u201cHow big data can take the pain out of performance reviews,\u201d <\/span><i><span style=\"font-weight: 400\">Fortune, <\/span><\/i><span style=\"font-weight: 400\">Oct 9, 2015, <\/span><a href=\"http:\/\/fortune.com\/2015\/10\/09\/big-data-performance-review\/\"><span style=\"font-weight: 400\">http:\/\/fortune.com\/2015\/10\/09\/big-data-performance-review\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[9] Dave Zielinski, \u201cArtificial Intelligence and Employee Feedback\u201d, <\/span><i><span style=\"font-weight: 400\">Society for Human Resource Management, <\/span><\/i><span style=\"font-weight: 400\">May 15 2017, <\/span><a href=\"https:\/\/www.shrm.org\/resourcesandtools\/hr-topics\/technology\/pages\/-artificial-intelligence-and-employee-feedback.aspx\"><span style=\"font-weight: 400\">https:\/\/www.shrm.org\/resourcesandtools\/hr-topics\/technology\/pages\/-artificial-intelligence-and-employee-feedback.aspx<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[10] Frank Partnoy, \u201cWhat Your Boss Could Learn by Reading the Whole Company\u2019s Emails\u201d. <\/span><i><span style=\"font-weight: 400\">The Atlantic, <\/span><\/i><span style=\"font-weight: 400\">Sep 2018, https:\/\/www.theatlantic.com\/magazine\/archive\/2018\/09\/the-secrets-in-your-inbox\/565745\/, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[11] Michael Rochelle and Friedman, \u201cLinkedIn Acquires Glint, Bolstering Its Position as an HCM Market-Maker\u201d, <\/span><i><span style=\"font-weight: 400\">Human Resources Today<\/span><\/i><span style=\"font-weight: 400\">, Oct 15, 2018. <\/span><a href=\"http:\/\/www.humanresourcestoday.com\/data\/glint\/survey\/?open-article-id=9072138&amp;article-title=linkedin-and-glint---potential-hcm-technology-powerhouse-in-the-making\"><span style=\"font-weight: 400\">http:\/\/www.humanresourcestoday.com\/data\/glint\/survey\/?open-article-id=9072138&amp;article-title=linkedin-and-glint&#8212;potential-hcm-technology-powerhouse-in-the-making<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[12] Seth Grimes, \u201cWhere are the text analytics unicorns?\u201d <\/span><i><span style=\"font-weight: 400\">VentureBeat<\/span><\/i><span style=\"font-weight: 400\">, May 3, 2015. <\/span><a href=\"https:\/\/venturebeat.com\/2015\/05\/03\/where-are-the-text-analytics-unicorns\/\"><span style=\"font-weight: 400\">https:\/\/venturebeat.com\/2015\/05\/03\/where-are-the-text-analytics-unicorns\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><span style=\"font-weight: 400\">[13] Sanati, \u201cHow big data can take the pain out of performance reviews\u201d.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Employees give feedback and comments to express how they&#039;re feeling. Can vendors specializing in natural language processing help organizations scale their ability to understand this data?<\/p>\n","protected":false},"author":11487,"featured_media":36757,"comment_status":"open","ping_status":"closed","template":"","categories":[407,193,751,346,4602,2311,5162,5161],"class_list":["post-36752","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-employee-satisfaction","category-employees","category-hr","category-machine-learning","category-natural-language-processing","category-people-analytics","category-performance-review","category-survey","hck-taxonomy-organization-ultimate-software","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Turning Feelings into Data: Applying Natural Language Processing to Employee Sentiment - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/turning-feelings-into-data-applying-natural-language-processing-to-employee-sentiment\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Turning Feelings into Data: Applying Natural Language Processing to Employee Sentiment - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Employees give feedback and comments to express how they&#039;re feeling. 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