Named Entity and Theme Sentiment Analysis
Except for press releases, it’s rare to find a document that is homogenously positive or negative. Sentiment is typically a localized phenomenon that is more appropriately computed at the paragraph, sentence or named entity level. Consider the following example:
"Julie Jones superb performance in the gubernatorial debate has all but assured her of victory in the upcoming elections. Unfortunately, the evening did not go as well for her opponent John Adams. Mr. Adams nervous and uncertain performance has put his entire political future into question."
The sentiment of this sentence is completely different for the two individuals described within, while the overall sentiment for the sentence averages out to roughly neutral.
Let’s look at the results of this snippet at the overall level and at the named entity level (red are negative sentiment bearing phrases, green are positive):
Sentiment Tagged Text
The tagged text yields a document sentiment of 0.11 which is squarely in the middle of the neutral range, and doesn’t really tell us anything about the real tone of this snippet.
Now let’s examine the named entity level sentiments for each person, and start by identifying every instance where the person is mentioned. Again, how we determine just what constitutes an entity is somewhat beyond this document, but isn’t important for understanding how we assign sentiment:
Entity Tagged Text
In the above-tagged text, notice that the system identifies not only the people by name, but also identifies the pronouns “her” and “his” (and will correctly associate the “her” with Julie Jones – an operation called “pronominal co-referencing”. Correctly including the pronouns significantly improves our software’s ability to measure the tone for each individual. Running this block through and computing sentiment analysis for each entity yields:
Julie Jones: Positive (+)0.22
This capability sets our core sentiment analysis software apart from other semantic analysis and text analysis tools, and enables our software to focus on the sentiment or tone of specific people, companies or products. The true value of sentiment analysis is in applying the measure to the people or products you’re concerned about. For most rich content sources, it’s much more important to measure and compare the sentiment of an individual named entity than it is to get the overall sentiment of the document. (Consider the case of a product review article, where one product gets panned and the other doesn’t – if you can’t discern which was which, then it doesn’t do you any good).
Contact Lexalytics today to learn more about our named entity and theme sentiment analysis software.