Abstract
Word embedding is a technique to represent sentences in vector space. The representation itself is carried-out to build a model that would suffice in representing a particular task related to the use of the sentence itself, for example, a model of similarity among sentences/words, a model of Twitter user connectivity, and demographics of tweets model. The use of word embedding is a handful to the sentiment analysis research because it helps build a mathematical-friendly model from sentences. The model then will be suitable as feeds for the other computational process.
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