  The presented work aims at generating a systematically annotated corpus that
can support the enhancement of sentiment analysis tasks in Telugu using
word-level sentiment annotations. From OntoSenseNet, we extracted 11,000
adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by
language experts. We discuss the methodology followed for the polarity
annotations and validate the developed resource. This work aims at developing a
benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a
model where lexeme annotations are applied for sentiment predictions. The
fundamental aim of this paper is to validate and study the possibility of
utilizing machine learning algorithms, word-level sentiment annotations in the
task of automated sentiment identification. Furthermore, accuracy is improved
by annotating the bi-grams extracted from the target corpus.
