Sentiment
A sentiment attribute flags a sentence as having either a positive or negative sentiment. Sentiment terms are highly dependent on the kind of texts being analyzed. For example, in a customer perception survey context the following terms might be flagged with a sentiment attribute:
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The words “avoid”, “terrible”, “difficult”, “hated” convey a negative sentiment.
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The words “attractive”, “simple”, ”self-evident”, “useful”, “improved” convey a positive sentiment.
Because sentiment terms are often specific to the nature of the source texts, InterSystems NLP only identifies a small set of sentiment terms automatically. You can flag additional words as having a positive sentiment or a negative sentiment attribute. You can specify a sentiment attribute for specific words using a User Dictionary. When source texts are loaded into a domain, each appearance of these terms and the part of the sentence affected by it is flagged with the specified positive or negative sentiment marker.
For example, if “hated” is specified as having a negative sentiment attribute, and “amazing” is specified as having a positive sentiment attribute, when InterSystems NLP applies them to the sentence:
I hated the rain outside, but the running shoes were amazing.
Negative sentiment would affect “rain” and positive sentiment would affect “running shoes”.
When a positive or negative sentiment attribute appears in a negated part of a sentence, the sense of the sentiment is reversed. For example, if the word “good” is flagged as a positive sentiment, the sentence “The coffee was good” is a positive sentiment, but the sentence “The coffee was not good” is a negative sentiment.
