The success of any organization depends upon its management’s vision and mission. And to meet their goals, they need productive and efficient workforce in place. Employee satisfaction plays a major role in creating a vibrant positive workplace. Employee engagement survey is one of the key approaches that organizations generally take to measure employee satisfaction. Employees are known to be the assets of any organization. So, it is necessary for any organization to listen to them to increase their productivity and efficiency, which in turn makes positive vibes to the organization. A carefully designed and conducted employee survey can reveal a great deal of information about employee perceptions that management can use to improve the workplace culture. Analysis and extraction of insights from the survey report is extremely necessary in today's world for any organization. If you do not follow your employee feedback and make changes accordingly in the workplace or take necessary actions, your organization will fail to make an impact.
So, you should analyze how your employees are thinking about your organization. One option is to conduct employee survey. And HR people can check the feedback captured through the survey. But is it possible to go through each individual feedback from millions of reviews? For a human being, it's next to impossible to review different kinds of feedback and take a decision. Every organization would like to get rid of this situation and try to use a technique that would decrease their effort and time. Here Sentiment Analysis comes into the picture.
What is sentiment analysis?
Sentiment Analysis is a technique to analyze the sentiment from the sentences; it could be positive, negative, or neutral in general. It's also called opinion mining. It is playing a crucial role in research as well as commercial purpose to determine the attitude or the emotion of the writer. We are using Sentiment Analysis to reduce the time in checking every line of a review. Instead of that we are going to extract sentiment of every review to analyze the attitude of employees towards the organization. Analyzing those, necessary actions can be taken to make the organization more employee friendly.
Organizations use sentiment analysis to get insight into their employees and set their HR strategies accordingly to improve their employee-centric services. Some important features of using sentiment analysis are:
To reduce human efforts to analyze employee reviews, artificial intelligence can be used to identify whether feedback is positive or negative. AI will also learn toxic words from the historical data and recommend a passage in carrying either positive feedback or negative based upon the feedback's word composition. This can be considered as a supervised learning problem where all historical data can be used to train the model. We can use any sequence-based supervised learning technique to train the models. Later, this trained model will be used to predict if a passage is carrying positive feedback, negative feedback or even neutral. This NLP and ML- based approach will help any organization to reduce human interventions in reviewing every feedback, instead they will get sentiment of each review, and based on that they will take necessary strategies to make their services more employee focused.
Business is expanding, so is data. Without data we can’t analyze our business bottlenecks, we can’t point out those fields where we must put extra effort to make organization more employee friendly. Human intervention is good and error-less up to a certain level. But when it comes for millions of data, we do not have that many resources. So, we must rely upon AI-based Techniques. Most of the organizations, these days rely heavily on social media to improve the employee experience. So, Sentiment Analysis can be their weapon to adopt more employee-friendly business strategies. But we must keep in mind that this approach can cause error in some cases. It mainly occurs when we are trying to extract the sentiment of a sarcastic comment. Sometimes it classifies a comment as a positive, although it's negative by means as it involves sarcasm. In such cases we must rely upon manual effort. Automated Sentiment analysis tools are good up to a certain case, but we must use them wisely. Making this model a base, organization can upgrade this to real-time to gain most from it.