Data annotation is an important but time-consuming and costly procedure. To
sort a text into two classes, the very first thing we need is a good annotation
guideline, establishing what is required to qualify for each class. In the
literature, the difficulties associated with an appropriate data annotation has
been underestimated. In this paper, we present a novel approach to
automatically construct an annotated sentiment corpus for Algerian dialect (a
Maghrebi Arabic dialect). The construction of this corpus is based on an
Algerian sentiment lexicon that is also constructed automatically. The
presented work deals with the two widely used scripts on Arabic social media:
Arabic and Arabizi. The proposed approach automatically constructs a sentiment
corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to
Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi
test sets, respectively. Ongoing work is aimed at integrating transliteration
process for Arabizi messages to further improve the obtained results.

本文提出了一种自动构建阿尔及利亚方言情感语料库的新方法，该语料库基于自动构建的阿尔及利亚情感词典，涉及使用阿拉伯文和阿拉伯字母拼音写成的社交媒体数据，并且取得了较好的实验结果。

SentiALG：阿尔及利亚情感分析自动语料标注

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

Sentiment Analysis (SA) is a major field of study in natural language
processing, computational linguistics and information retrieval. Interest in SA
has been constantly growing in both academia and industry over the recent
years. Moreover, there is an increasing need for generating appropriate
resources and datasets in particular for low resource languages including
Persian. These datasets play an important role in designing and developing
appropriate opinion mining platforms using supervised, semi-supervised or
unsupervised methods. In this paper, we outline the entire process of
developing a manually annotated sentiment corpus, SentiPers, which covers
formal and informal written contemporary Persian. To the best of our knowledge,
SentiPers is a unique sentiment corpus with such a rich annotation in three
different levels including document-level, sentence-level, and
entity/aspect-level for Persian. The corpus contains more than 26000 sentences
of users opinions from digital product domain and benefits from special
characteristics such as quantifying the positiveness or negativity of an
opinion through assigning a number within a specific range to any given
sentence. Furthermore, we present statistics on various components of our
corpus as well as studying the inter-annotator agreement among the annotators.
Finally, some of the challenges that we faced during the annotation process
will be discussed as well.

本文主要研究是针对波斯语的情感分析和意见挖掘，详细阐述了研制手动标注的 SentiPers 情感语料库的全过程，并介绍了该语料库在文档级别、句子级别和实体 / 方面级别的独特情感注释方式，以及波斯语情感文本数据资源的不足。