Sentiment Classification of Textual Content using Hybrid DNN and SVM Models

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The proliferation of Web 2.0 has resulted in a deluge of real-time, unstructured data such as user comments, opinions, and likes. The lack of structure in the data makes it difficult to create a reliable prediction model for sentiment analysis. There have been promising applications of several DNN architectures to sentiment analysis, however, these methods tend to treat all features identically and struggle with high-dimensional feature spaces. In addition, existing techniques fail to effectively combine semantic and sentiment knowledge for the purpose of extracting meaningful relevant contextual sentiment characteristics. This paper proposes an integrated convolutional neural network, or CNN, architecture that takes sentiment as well as context into consideration as a means of intelligently developing and highlighting significant components of relevant sentiment contextual in the text. To start, we use transformers' bidirectional encoder representations to create sentiment-enhanced embeddings of words for text semantic extraction using integrated emotion lexicons with broad coverage for feature identification. The proposed approach then adjusts the CNN in a way that it can detect both word order/contextual text semantics data as well as the long-dependency relationship in the phrase sequence. Our approach also employs a system to prioritize the most important portions of the phrase sequence. One last step in sentiment analysis is the use of support vector machines (SVMs) to reduce the complexity of the space of features and identify locally significant characteristics. The accuracy of existing text sentiment categorization is greatly improved by the use of the proposed model, as shown by an evaluation of real-world benchmark datasets.