Prediction of Sentiment from Macaronic Reviews

Abstract

Web-sphere is the vast ocean of data. It allows its users to write their opinion, suggestions over various social platforms. The users often prefer to write in their native language or some hybrid content (i.e., combination of two or more languages). It’s also observed that people use a word or two of their native language in a text of base language. The presence of native words along with base language is known as macaronic languages. For example: Dunglish (Dutch and English), Chinglish (Chinese and English), Hinglish (Hindi and English) The use of macaronic languages over the web is on the rise these days. This type of text generally doesn’t follow any syntactic structure, thus making processing of the content difficult. This paper deals with extracting meaningful information of a text containing macaronic content. It also facilitates the need of expert analysers for the processing of such content to take effective decisions. The performance of various decision support systems is dependable over these analysers. Therefore, this paper presents an algorithm which initially normalizes the content to its base language; later performs sentiment analysis over it. The experimental results using proposed algorithm indicates a trade-off between various performance aspects.