Sentiment analysis is an active topic in Natural Language Processing (NLP). It has attracted a significant interest of research community due to the wide range of applications, including social-media, fake news spotting and interactive applications. In this paper, we present a novel approach for semiautomatic background creation and conspiracy classification. For this purpose, a complete framework including novel recurrent models is proposed. The BORJIS: Best algorithm foR Joint conspiracy and sarcasm detection has been tested on twitter-crawled data and It is composed by: (a) the crawler and labelling module, (b) the features vector extraction and (c) the conspiracy classifier. BORJIS is established as a novel approach for processing variable length inputs to detect conspiracy. Both the data and the code are referenced in the article.