Improving Sentiment Classification of Social Media Posts through Data Refinements
published: Nov. 7, 2016, recorded: August 2016, views: 1082
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Quality training data is essential for building high performance machine learning models. However, certain types of tasks such as opinion mining are inherently subjective, making it hard to elicit reliable judgements from human annotators. The problem is further exacerbated in situations where opinions are elicited on short text such as Tweets or micro reviews containing only one or two lines. The talk addresses various means of circumventing these challenges via automation of some annotation tasks as well as setting up multiple experiments for collecting human judgements.
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