Archit Yajnik and Sabu Lama Tamang, Manipal Institute of Technology, India

The article represents the Sentiment Analysis (SA) of a Nepali sentence.Skip-gram model is used for the word to vector encoding. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 score of 0.6779 is observedfor positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences.

skip -gram model, MLP classification, parser, verb chunks