Abstract
With the current upsurge in the usage of social media platforms, the trend of using short text (microtext) in place of text with standard words has seen a significant rise. The usage of microtext poses a considerable performance issue to 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 (OOV) concepts into their standard in-vocabulary (IV) form. We develop binary classifier to detect OOV sentences and then they are transformed to phoneme subspace using grapheme to phoneme converter. We compare the phonetic and string distance using the Sorensen similarity algorithm. The phonetically similar IV 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 improves the accuracy of polarity detection by 6% as compared to the earlier model. In conclusion, we apply a grapheme to phoneme converter for microtext normalization and show its application on sentiment analysis.
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Satapathy, R., Singh, A., Cambria, E. (2019). PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_20
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