Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on Twitter dataset and robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively
