[Talk Summary 4] Entity/Event-level Sentiment Detection and Inference
Lingjia Deng from Intelligent Systems Program, University of Pittsburgh had a talk about Entity/Event-level Sentiment Detection and Inference on October, 07, 2016.
Lingjia's work focused on sentiment analysis and opinion mining. She introduced an sentiment analysis model that aims at detecting both explicit and implicit sentiments expressed among entities and events in text.
As stated in the talk, most of the work in opinion mining focuses on extracting explicit sentiments. For example, in the sentence "she loves traveling", the sentiment that is explicitly expressed is positive. However, in another example, "People celebrate that Trump was defeated." there are two opinions in this sentence. One is also positive, based on the word celebrate, people celebrate something. The other is negative, based on the word defeated, Trump was defeated. But besides these explicit opinions, we can infer implicitly another sentiment in the sentence which is that people is negative toward Trump.
In order to detect both explicit and implicit entity/event-level sentiments, Lingjia embedded the inference rules and incorporated +/-effect event information into a computational framework. She also introduced a corpus of Entity/Event-level Sentiment, MPQA 3.0 that is developed from MPQA 2.0 (Wiebe et al., 2005; Wilson, 2007). MPQA 3.0 added entity-target and event-target (eTarget) annotations using for extracting targets in the sentence (the important component of an opinion).
I find the talk very interesting. It performs a model and techniques to improve detecting entity/event-level sentiments in the documents. If the work could leverage some NLP techniques, it would be developed to extract more complicated sentiments and opinions in long paragraphs, joint sentences.
10/07/2016
5317 Sennott Square
University of Pittsburgh
Lingjia's work focused on sentiment analysis and opinion mining. She introduced an sentiment analysis model that aims at detecting both explicit and implicit sentiments expressed among entities and events in text.
As stated in the talk, most of the work in opinion mining focuses on extracting explicit sentiments. For example, in the sentence "she loves traveling", the sentiment that is explicitly expressed is positive. However, in another example, "People celebrate that Trump was defeated." there are two opinions in this sentence. One is also positive, based on the word celebrate, people celebrate something. The other is negative, based on the word defeated, Trump was defeated. But besides these explicit opinions, we can infer implicitly another sentiment in the sentence which is that people is negative toward Trump.
In order to detect both explicit and implicit entity/event-level sentiments, Lingjia embedded the inference rules and incorporated +/-effect event information into a computational framework. She also introduced a corpus of Entity/Event-level Sentiment, MPQA 3.0 that is developed from MPQA 2.0 (Wiebe et al., 2005; Wilson, 2007). MPQA 3.0 added entity-target and event-target (eTarget) annotations using for extracting targets in the sentence (the important component of an opinion).
I find the talk very interesting. It performs a model and techniques to improve detecting entity/event-level sentiments in the documents. If the work could leverage some NLP techniques, it would be developed to extract more complicated sentiments and opinions in long paragraphs, joint sentences.
10/07/2016
5317 Sennott Square
University of Pittsburgh
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