SentiPers: A Sentiment Analysis Corpus for Persian

TL;DR

This paper introduces SentiPers, a comprehensive manually annotated sentiment corpus for Persian, covering formal and informal texts with multi-level annotations, aiding sentiment analysis research for low-resource languages.

Contribution

It presents the creation of a unique Persian sentiment corpus with multi-level annotations, including a large dataset of over 26,000 sentences with sentiment scores.

Findings

Rich annotation levels including document, sentence, and entity/aspect.

The corpus contains over 26,000 sentences from digital product opinions.

Inter-annotator agreement and annotation challenges are analyzed.

Abstract

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.