# PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis

## TL;DR

This paper introduces a phonetic-based normalization method for microtext in social media, improving concept-level sentiment analysis accuracy by transforming microtext into standard vocabulary using a combined AI approach.

## Contribution

It presents a novel framework that couples phonetic similarity with machine learning to normalize microtext, enhancing sentiment polarity detection accuracy.

## Key findings

- Achieved a 6% increase in polarity detection accuracy.
- Validated microtext normalization as essential for better sentiment analysis.
- Demonstrated effectiveness of phonetic similarity in concept normalization.

## Abstract

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.