Sentiment Analysis Using Averaged Weighted Word Vector Features
Table 1: Accuracies of Methods Before Ensemble for IMDB dataset
['[BOLD] Methods', '[BOLD] Accuracies', '[BOLD] Explanation']
The method that employs weighted word vectors achieves the highest performance. This shows that the positive and negative strengths of words obtained from the data affect on determining the correct sentiment. Since the ARV and WARV models having separate word2vec and glove vectors, we use only these 600-dimensional versions in the experiments that follow. Our WARV model has 94.95 % accuracy. It means that it misclassified 1263 reviews out of 25000 reviews.