  In order to maximize the applicability of sentiment analysis results, it is
necessary to not only classify the overall sentiment (positive/negative) of a
given document but also to identify the main words that contribute to the
classification. However, most datasets for sentiment analysis only have the
sentiment label for each document or sentence. In other words, there is no
information about which words play an important role in sentiment
classification. In this paper, we propose a method for identifying key words
discriminating positive and negative sentences by using a weakly supervised
learning method based on a convolutional neural network (CNN). In our model,
each word is represented as a continuous-valued vector and each sentence is
represented as a matrix whose rows correspond to the word vector used in the
sentence. Then, the CNN model is trained using these sentence matrices as
inputs and the sentiment labels as the output. Once the CNN model is trained,
we implement the word attention mechanism that identifies high-contributing
words to classification results with a class activation map, using the weights
from the fully connected layer at the end of the learned CNN model. In order to
verify the proposed methodology, we evaluated the classification accuracy and
inclusion rate of polarity words using two movie review datasets. Experimental
result show that the proposed model can not only correctly classify the
sentence polarity but also successfully identify the corresponding words with
high polarity scores.
