Salience 4.3: Opinion Mining

  1 m, 38 s

One of the two major new features in Salience 4.3 (releasing around June 30th) is "opinion mining". Opinion mining expands our core technology to handle indirect quotes. We've been able to extract quote-mark delimited quotes for a while now, and you could perform further analysis on those quotes (which were attached to the speaker). Opinion mining means that Salience 4.3 can now handle sentences like: 1) Seth then asserted that this was a truly awesome feature. 2) Tim agreed that Bill was unduly angry. 3) Paul explained that the code was broken. In each case there is a speaker, a topic, and sentiment expressed. The "speaker" is always an entity - and it could be a place, person, or company. The topic can be either a theme or an entity. Sentiment is assigned to the topic. Thus, in sentence 1: Speaker: Seth Topic: awesome feature Sentiment: positive Sentence 2: Speaker: Tim Topic: Bill Sentiment: negative Sentence 3: Speaker: Paul Topic: code Sentiment: negative How does this work? I'm glad you asked. We have a data directory full of patterns for opinions. These basically come down to the following 3 classes: 1) "attributed" opinions (e.g. Paul said "This is great") 2) "cross sentence" opinions ("This is Great." Said Paul) 3) "unattributed" opinions (the examples above) Unattributed uses a list of verbs that are expected to express an opinion, and looks for certain patterns using those. There are roughly 200 verbs that clearly express opinion (acknowledge, accuse, add, admit, advise, affirm, allege, answer...) and roughly 200 that have additional requirements because they indicate opinion only in certain contexts: (accept, account for, address, agree, allow, analyze...). To give an example of how this works, consider the following: "Paul charged at George." vs. "Paul charged that George was incompetent." The "that" in the second sentence changes "charged" from being an action to indicating an opinion. For those who update to 4.3, check the data directory: /data/opinions/*.ptn for all the patterns. Try it out... we think that your opinion on this will be positive.

Categories: Sentiment Analysis, Text Analytics