An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed DataThis work was presented at 1^{st} Workshop on Humanizing AI (HAI) at IJCAI’18 in Stockholm, Sweden.
Table 1: Hyperparameters of LSTM classifier
['[BOLD] Hyperparameter', '[BOLD] Value']
For the 3-class sentiment classification of the code mixed data, we designed a LSTM based classifier with the following details. Input Features : Each token is represented as a bag-of-character-trigrams vector. We allowed a maximum sequence of 100 character trigram features and applied truncation and padding in case of excess and deficit tokens respectively. For every token, we fed a 128 length embedding matrix to the LSTM unit. This feature is a fair representation of the sparse code-mixed data as it helps to solve out-of-vocabulary issues and removes the influence of the word stems, diverse variations and contractions that arises during conversion of Hindi to romanized code-mixed data. Output : The output of the end state of the final LSTM layer is connected to a Fully Connected (FC) layer which models the interactions between these features and the classes . A softmax activation function is used to produce correctly normalized probability values. Loss function : We train the parameters of the classifier with an objective of maximizing their predication accuracy given the target labels in the training set or minimizing the cross entropy error across the set. If t is the true label and o is the output of the network, the cross entropy (CE) loss function is calculated as follows: CE(t,o)=−(tlog(o)+(1−t)log(1−o)) (4)