Abstract:
Detection of hate speech is part of sentiment analysis and sentiment analysis refers to the task
of natural language processing to determine whether a piece of text contains some subjective
information and what subjective information it expresses, that is, whether the attitude behind
this text is positive, negative or neutral. Understanding the opinions behind user-generated
content automatically is of great help for commercial, social and political use, among others.
The task can be conducted on different levels, classifying the polarity of words, sentences or
entire documents. It is one of the most active research areas in natural language processing and
text mining in recent years. Its popularity is mainly due to two reasons. First, it has a wide
range of applications because opinions are central to almost all human activities and are key
influencers of our behaviours. Whenever we need to make a decision, we want to hear others’
opinions. Secondly, it presents many challenging research problems. Part of the reason for the
lack of study before was that there was little opinionated text in digital forms. It is thus no
surprise that the inception and the rapid growth of the field coincide with those of the social
media on the Web. In fact, the research has also spread outside of computer science to
management sciences and social sciences due to its importance to business and society as a
whole.
The attempt done by this research is to detect hate content that in textual content uploaded to
or exchanged through social media and are primarily in Sinhala or Singlish context. Here, the
detection will not be restricted to one domain since the main aim of this research is to capture
any hate content irrespective of the domain. Many previously used machine learning techniques
used for mainly English and other native languages were researched in order to get the basic
idea on how to perform sentiment analysis from the scratch.
