BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations 1footnote 11footnote 1* This work was presented at Student Research Workshop in 56^{th} Annual Meeting of the Association for Computational Linguistics, ACL.
Table 2: Comparison of accuracies obtained through majority polling on different resources.
['[EMPTY]', '[BOLD] SentiWordNet', '[BOLD] Our resource', '[BOLD] Bigram', '[BOLD] Uni+Bigrams']
High accuracy obtained by using only bigrams for majority polling proves our hypothesis. However, there is a trade-off between coverage and accuracy. This can be depicted from the huge increase in the count of unclassified reviews in case of bigram majority poling. We also observe that effect of morphological segmentation on accuracy is hardly positive. This indicates that in case of Telugu, morphological data has relevance to sentiment expressed and morphological segmentation would result in loss of such valuable information for sentiment analysis tasks.