SEMANTIC ANALYSIS
Use of Semanticase for the analysis of open responses in the climate survey
Sentiment analysis for the detection of the perception of personnel with respect to the factors that influence the organizational climate, such as the environment, organization, work activities and relationships, well-being and safety. The purpose of the survey was to understand the factors that regulate the relationship between the company and the staff, to fully understand the opinions expressed and "experiences".
Case study
MEASURING THE SENTIMENT OF EMPLOYEES IN ENEL'S CLIMATE SURVEY
Analyzing open comments to understand the nuances of employee judgment in the climate survey was the goal of Enel's collaboration in the Piazza Copernico research project.
The number of comments collected in the very large organizations requires a considerable effort of commitment and time for reading, as well as a significant agreement in the criteria of analysis by the evaluators. For this reason, Enel has decided to experiment with Piazza Copernico the application of semantic techniques and sentiment analysis to understand, measure and compare opinions in a statistically valid way and reduce the overall effort.
To achieve the goal, the following were applied:
- the semantic algorithm based on Structural Topic Model;
- the sentiment analysis algorithm.
The first made it possible to identify the most representative content structure on a probabilistic basis. Through the reasoned reading of the structure of the topics elaborated, it was possible to identify the "hot" issues and understand all their associated meanings.
Subsequently, the sentiment analysis was carried out for each comment, ie the verification of the polarity (positive or negative) of the opinions expressed in the textual comments. This analysis made it possible to understand with which intrinsic evaluation the lyrics had been written. In addition, sentiment indices diversified by role, age, gender, seniority and team leadership were elaborated, thus making it possible to evaluate the different judgments for each category.
In conclusion, this analysis made it possible to read the open contents of the climate survey through a meaningful synthesis of them and to better understand the associated judgment and the forms of communication in which it is expressed.
PIAZZA COPERNICO LAB, this application was significant for the application of semantic algorithms in the field of open questionnaires (training and otherwise), surveys, community analysis, contests, and any other sphere of written expression of content and opinions.
