@InProceedings{10.1007/978-3-319-75487-1_19,
author="Akhtar, Md Shad
and Ekbal, Asif
and Bhattacharyya, Pushpak",
editor="Gelbukh, Alexander",
title="Aspect Based Sentiment Analysis: Category Detection and Sentiment Classification for Hindi",
booktitle="Computational Linguistics and Intelligent Text Processing",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="246--257",
abstract="E-commerce markets in developing countries (e.g. India) have witnessed a tremendous amount of user's interest recently. Product reviews are now being generated daily in huge amount. Classifying the sentiment expressed in a user generated text/review into certain categories of interest, for example, positive or negative is famously known as sentiment analysis. Whereas aspect based sentiment analysis (ABSA) deals with the sentiment classification of a review towards some aspects or attributes or features. In this paper we asses the challenges and provide a benchmark setup for aspect category detection and sentiment classification for Hindi. Aspect category can be seen as the generalization of various aspects that are discussed in a review. As far as our knowledge is concerned, this is the very first attempt for such kind of task involving any Indian language. The key contributions of the present work are two-fold, viz. providing a benchmark platform by creating annotated dataset for aspect category detection and sentiment classification, and developing supervised approaches for these two tasks that can be treated as a baseline model for further research.",
isbn="978-3-319-75487-1"
}