  Over the last few years, machine learning over graph structures has
manifested a significant enhancement in text mining applications such as event
detection, opinion mining, and news recommendation. One of the primary
challenges in this regard is structuring a graph that encodes and encompasses
the features of textual data for the effective machine learning algorithm.
Besides, exploration and exploiting of semantic relations is regarded as a
principal step in text mining applications. However, most of the traditional
text mining methods perform somewhat poor in terms of employing such relations.
In this paper, we propose a sentence-level graph-based text representation
which includes stop words to consider semantic and term relations. Then, we
employ a representation learning approach on the combined graphs of sentences
to extract the latent and continuous features of the documents. Eventually, the
learned features of the documents are fed into a deep neural network for the
sentiment classification task. The experimental results demonstrate that the
proposed method substantially outperforms the related sentiment analysis
approaches based on several benchmark datasets. Furthermore, our method can be
generalized on different datasets without any dependency on pre-trained word
embeddings.
