Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
This is an incremental improvement for sentiment analysis in social media, specifically for tweet polarity classification.
The paper tackled tweet sentiment classification by using ten convolutional neural networks with majority voting, achieving 67.4% accuracy and ranking fourth out of 38 systems in SemEval-2017 Task 4.
This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4%