Sentiment analysis is the process of using computational power to assess the subjective nature of text and to decide if it expresses a positive, negative or neutral sentiment in relation to a specific subject.
Summary by The World of Work Project
Sentiment Analysis
Sentiment involves getting computers to read blocks of text and decide if the blocks say positive, negative or neural things about their subjects. There are several different processes that can be used to do this assessment. That said, they all aim to produce the same thing: a clear understanding of the sentiment of blocks of text.
Sentiment Analysis In The World Of Work
There are many different uses for sentiment analysis in the workplace, and beyond. Wider uses include trying to understand what voters think about political candidates and trying to understand what consumers think about products.
From a world of work perspective though, the most interesting application of sentiment analysis is trying to understand how employees feel about their work and their organization by analyzing their written text either in emails, feedback portals or other locations. This, of course, requires permission to assess this data.
There are many different ways that organizations can go about doing this, including analysis of open feedback and survey data.
The World of Work Project View
We think that sentiment analysis in the workplace is fascinating as a subject and that it’s a powerful tool. It’s another example of the added insight that computers can bring us in the world of work. As our data sets increase and as computers become better at learning, we’ll get even better at it.
That said, we’re not sure that it’s really a good thing to read the text of employees in organizations. We’re undecided regarding the ethical acceptability of this. We know that sentiment analysis doesn’t need to attribute text to individuals, but we still worry about abuses of power when it comes to reading and analyzing large blocks of text.
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Sources and Feedback
The concepts behind this post is based on lectures we have attended, conversations with various podcast guests and general reading around the subject. There are no specific references for it.
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