Bi lingual Intent Classification of Twitter Posts: A Roadmap
A core advantage of social media platforms is the freedom that comes with the way users express their opinions and share information as they deem fit, in line with the subject of discussion. Advances in text analytics have allowed researchers to adequately classify information expressed in natural language text, which emanates in millions per minute, under well defined categories like "hate" or "radicalized" content which provide further insight into intent of the sender. This analysis is important for social media intelligence and information security. Commercial intent classifications have witnessed several research attentions. However, social intent classification of topics in line with hate, radicalized posts, have witnessed little research effort. The focus of this study is to develop a roadmap of a model for automatic bilingual intent classification of hate speech. This empirical model will involve the use of bi gram words for intent classification. The feature extraction will include expected cross entropy, while topic modeling will use supervised context based n gram approach. Classification will be done using ensemble based approach which will include the use of Naive Bayes and Support Vector Machine. This study will also discuss the differences between the concept of fake news, stance and intent identification. We anticipate that the proposed roadmap, if implemented, will be useful in the classification of intent as it relates to hate speech in bilingual twitter post. The proposed model has the potential to improve intent classification and that could be useful in hate speech detection, which can avert social or security problems.
SEDA