The result
In this case, the sentiment analysis tool was appropriate to test
the documents discarded throughout the review process to ensure maximum quality control.
Documents tagged as not relevant were held in a discard pile that was later subjected to concept clustering. Once we had distinct clusters of documents that appeared to be irrelevant to the case, we overlaid the clustering with sentiment analysis.
Sentiment analysis uses natural language processing (NLP), machine learning (ML) and artificial intelligence (AI) to analyse and determine the sentiment or emotion expressed in text or speech.
The tool can configure whether the overall sentiment is positive, negative or neutral. Or, in the case of RelativityOne software, either of the following sentiments: positivity, negativity, anger and desire.
In this case, we searched for all sentiments but paid particular attention to those flagging for anger or negativity.
Using these two tools in conjunction with one another allowed us to view clusters at a glance and determine whether they contained any emotions we highlighted as important. Given the nature of DSARs, the emotions we typically scan for are anger and negativity.
However, depending on the subject matter, this isn’t always the case. Within clusters where we found evidence of anger and negativity, we could pull out documents and review them for relevance before making any necessary adjustments to coding decisions.
