  Unsupervised sentiment analysis is traditionally performed by counting those
words in a text that are stored in a sentiment lexicon and then assigning a
label depending on the proportion of positive and negative words registered.
While these "counting" methods are considered to be beneficial as they rate a
text deterministically, their classification rates decrease when the analyzed
texts are short or the vocabulary differs from what the lexicon considers
default. The model proposed in this paper, called Lex2Sent, is an unsupervised
sentiment analysis method to improve the classification of sentiment lexicon
methods. For this purpose, a Doc2Vec-model is trained to determine the
distances between document embeddings and the embeddings of the positive and
negative part of a sentiment lexicon. These distances are then evaluated for
multiple executions of Doc2Vec on resampled documents and are averaged to
perform the classification task. For three benchmark datasets considered in
this paper, the proposed Lex2Sent outperforms every evaluated lexicon,
including state-of-the-art lexica like VADER or the Opinion Lexicon in terms of
classification rate.
