Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations†
AbstractThe research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved by providing the possibility of the joint usage of the following methods: (1) retrieval and recognition of the hierarchical semantic structure of the text and (2) development of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP. The main scientific contribution of this research is the set of the following approaches: at the phase of LSR revealing (1) combination of the discriminant and probabilistic models while applying the rules of adjustments to obtain the final joint result; at all SCP phases (2) considering document as a complex structure of topically completed textual components (paragraphs) and (3) taking into account the features of persuasive documents’ type. The experimental results have demonstrated the enhancement of the SCP accuracy, namely significant increase of average values of recall and precision indicators and guarantee of sufficient accuracy level. View Full-Text
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Rizun, N.; Taranenko, Y.; Waloszek, W. Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations. Information 2018, 9, 307.
Rizun N, Taranenko Y, Waloszek W. Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations. Information. 2018; 9(12):307.Chicago/Turabian Style
Rizun, Nina; Taranenko, Yurii; Waloszek, Wojciech. 2018. "Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations." Information 9, no. 12: 307.
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