Abstract:
The enormous number of user activity on online social networks results in a considerable amount of data which expresses the opinion from millions of people with diversity...Show MoreMetadata
Abstract:
The enormous number of user activity on online social networks results in a considerable amount of data which expresses the opinion from millions of people with diversity in their social aspects. The freedom of language usage shared through social media paves the way for the existence of code-mixed data that turns out to be more complex for mining the information out of it. Considering this, we created Kannada-English code mixed corpus by crawling Facebook comments. As of now, there is no relevant corpus as well as literature available for code-mixed Kannada-English sentiment analysis. In addition to the crawled corpus, we also used sentiment analysis code-mixed corpus provided by Sentiment Analysis for Indian Languages (SAIL)-2017 which includes Bengali-English and Hindi-English languages. This paper also addresses the performance of distributed representation methods in sentiment analysis task. We have reported comparisons among different machine learning and deep learning techniques.
Published in: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Date of Conference: 19-22 September 2018
Date Added to IEEE Xplore: 02 December 2018
ISBN Information: