Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis
This work addresses sentiment analysis for Telugu language users by incrementally enhancing lexical resources and sense-annotations.
The paper introduces a crowdsourcing tool to populate OntoSenseNet, a sentiment polarity annotated Telugu resource, and analyzes the importance of sense-annotations from it for sentiment analysis, including computing adverbial class distributions of verbs to aid word-sense disambiguation.
This paper illustrates the interface of the tool we developed for crowd sourcing and we explain the annotation procedure in detail. Our tool is named as 'Parupalli Padajaalam' which means web of words by Parupalli. The aim of this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu resource. Recent works have shown the importance of word-level annotations on sentiment analysis. With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis. We explain the fea- tures extracted from OntoSenseNet (Telugu). Furthermore we compute and explain the adverbial class distribution of verbs in OntoSenseNet. This task is known to aid in disambiguating word-senses which helps in enhancing the performance of word-sense disambiguation (WSD) task(s).