  Sentiment Analysis for Indian Languages (SAIL)-Code Mixed tools contest aimed
at identifying the sentence level sentiment polarity of the code-mixed dataset
of Indian languages pairs (Hi-En, Ben-Hi-En). Hi-En dataset is henceforth
referred to as HI-EN and Ben-Hi-En dataset as BN-EN respectively. For this, we
submitted four models for sentiment analysis of code-mixed HI-EN and BN-EN
datasets. The first model was an ensemble voting classifier consisting of three
classifiers - linear SVM, logistic regression and random forests while the
second one was a linear SVM. Both the models used TF-IDF feature vectors of
character n-grams where n ranged from 2 to 6. We used scikit-learn (sklearn)
machine learning library for implementing both the approaches. Run1 was
obtained from the voting classifier and Run2 used the linear SVM model for
producing the results. Out of the four submitted outputs Run2 outperformed Run1
in both the datasets. We finished first in the contest for both HI-EN with an
F-score of 0.569 and BN-EN with an F-score of 0.526.
