7 research outputs found
A review on corpus annotation for arabic sentiment analysis
Mining publicly available data for meaning and value is an important
research direction within social media analysis. To automatically analyze
collected textual data, a manual effort is needed for a successful machine learning algorithm to effectively classify text. This pertains to annotating the text adding labels to each data entry. Arabic is one of the languages that are growing rapidly in the research of sentiment analysis, despite limited resources and scares annotated corpora. In this paper, we review the annotation process carried out by those papers. A total of 27 papers were reviewed between the
years of 2010 and 2016
Phonetically rich and balanced text and speech corpora for Arabic language
This paper describes the preparation, recording, analyzing, and evaluation
of a new speech corpus for Modern Standard Arabic (MSA). The speech corpus
contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic
native speakers from 11 different Arab countries representing three major regions
(Levant, Gulf, and Africa). Three hundred and sixty seven sentences are considered as
phonetically rich and balanced, which are used for training Arabic Automatic Speech
Recognition (ASR) systems. The rich characteristic is in the sense that it must contain
all phonemes of Arabic language, whereas the balanced characteristic is in the sense
that it must preserve the phonetic distribution of Arabic language. The remaining 48
sentences are created fo