A comprehensive cross-language framework for harmful content detection
  with the aid of sentiment analysis
Mohammad Dehghani
In today's digital world, social media plays a significant role in
facilitating communication and content sharing. However, the exponential rise
in user-generated content has led to challenges in maintaining a respectful
online environment. In some cases, users have taken advantage of anonymity in
order to use harmful language, which can negatively affect the user experience
and pose serious social problems. Recognizing the limitations of manual
moderation, automatic detection systems have been developed to tackle this
problem. Nevertheless, several obstacles persist, including the absence of a
universal definition for harmful language, inadequate datasets across
languages, the need for detailed annotation guideline, and most importantly, a
comprehensive framework. This study aims to address these challenges by
introducing, for the first time, a detailed framework adaptable to any
language. This framework encompasses various aspects of harmful language
detection. A key component of the framework is the development of a general and
detailed annotation guideline. Additionally, the integration of sentiment
analysis represents a novel approach to enhancing harmful language detection.
Also, a definition of harmful language based on the review of different related
concepts is presented. To demonstrate the effectiveness of the proposed
framework, its implementation in a challenging low-resource language is
conducted. We collected a Persian dataset and applied the annotation guideline
for harmful detection and sentiment analysis. Next, we present baseline
experiments utilizing machine and deep learning methods to set benchmarks.
Results prove the framework's high performance, achieving an accuracy of 99.4%
in offensive language detection and 66.2% in sentiment analysis.