  With the current upsurge in the usage of social media platforms, the trend of
using short text (microtext) in place of standard words has seen a significant
rise. The usage of microtext poses a considerable performance issue in
concept-level sentiment analysis, since models are trained on standard words.
This paper discusses the impact of coupling sub-symbolic (phonetics) with
symbolic (machine learning) Artificial Intelligence to transform the
out-of-vocabulary concepts into their standard in-vocabulary form. The phonetic
distance is calculated using the Sorensen similarity algorithm. The
phonetically similar invocabulary concepts thus obtained are then used to
compute the correct polarity value, which was previously being miscalculated
because of the presence of microtext. Our proposed framework increases the
accuracy of polarity detection by 6% as compared to the earlier model. This
also validates the fact that microtext normalization is a necessary
pre-requisite for the sentiment analysis task.
