@inbook{00bce4028b11474391677a0bc7fb7646,
title = "Unsupervised fine-grained sentiment analysis system using lexicons and concepts",
abstract = "Sentiment is mainly analyzed at a document, sentence or aspect level. Document or sentence levels could be too coarse since polar opinions can co-occur even within the same sentence. In aspect level sentiment analysis often opinion-bearing terms can convey polar sentiment in different contexts. Consider the following laptop review: “the big plus was a large screen but having a large battery made me change my mind,” where polar opinions co-occur in the same sentence, and the opinion term that describes the opinion targets (“large”) encodes polar sentiments: a positive for screen, and a negative for battery. To parse these differences, our approach is to identify opinions with respect to the specific opinion targets, while taking the context into account. Moreover, considering that there is a problem of obtaining an annotated training set in each context, our approach uses unlabeled data.",
keywords = "Fine-grained sentiment analysis, Lexicon, Opinion mining",
author = "Nir Ofek and Lior Rokach",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.",
year = "2014",
month = oct,
day = "4",
doi = "10.1007/978-3-319-12024-9_3",
language = "English",
isbn = "978-3-319-12023-2",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "28--33",
editor = "{Di Noia}, Tommaso and Valentina Presutti and Recupero, {Diego Reforgiato} and Iv{\'a}n Cantador and Christoph Lange and Christoph Lange and Anna Tordai and Christoph Lange and Milan Stankovic and Erik Cambria and {Di Iorio}, Angelo",
booktitle = "Semantic Web Evaluation Challenge - SemWebEval 2014 at ESWC 2014, Revised Selected Papers",
address = "Germany",
}