@InProceedings{sweeney-padmanabhan:2017:RANLP,
  author    = {Sweeney, Colm  and  Padmanabhan, Deepak},
  title     = {Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {733--740},
  abstract  = {The sentiment analysis task has been traditionally divided into lexicon or
	machine learning approaches, but recently the use of word embeddings methods
	have emerged, that provide powerful algorithms to allow semantic understanding
	without the task of creating large amounts of annotated test data. One problem
	with this type of binary classification, is that the sentiment output will be
	in the form of ‘1’ (positive) or ‘0’ (negative) for the string of text
	in the tweet, regardless if there are one or more entities referred to in the
	text. This paper plans to enhance the word embeddings approach with the
	deployment of a sentiment lexicon-based technique to appoint a total score that
	indicates the polarity of opinion in relation to a particular entity or
	entities. This type of sentiment classification is a way of associating a given
	entity with the adjectives, adverbs, and verbs describing it, and extracting
	the associated sentiment to try and infer if the text is positive or negative
	in relation to the entity or entities.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_094}
}