@article{2639,
  author = {Salima BEHDENNA, Fatiha Barigou, Ghalem Belalem},
  title = {Sentiment Analysis of Arabic Tweets: Opinion Target Extraction},
  journal = {Journal of Digital Information Management},
  year = {2018},
  volume = {16},
  number = {6},
  doi = {10.6025/jdim/2018/16/6/324-331},
  url = {http://dline.info/fpaper/jdim/v16i6/jdimv16i6_4.pdf},
  abstract = {Due to the increased volume of Arabic
opinionated posts on different social media, Arabic
sentiment analysis is viewed as an important research
field. Identifying the target or the topic on which opinion
has been expressed is the aim of this work. Opinion target
identification is a problem that was generally very little
treated in Arabic text. In this paper, an opinion target
extraction method from Arabic tweets is proposed. First,
as a preprocessing phase, several feature forms from
tweets are extracted to be examined. The aim of these
forms is to evaluate their impacts on accuracy. Then, two
classifiers, SVM and Naive Bayes are trained. The
experiment results show that, with 500 tweets collected
and manually tagged, SVM gives the highest precision
and recall (86%).},
}