Deep finesse network model with multichannel syntactic and contextual features for target-specific sentiment classification - Edara, Deepak Chowdary; Sistla, Venkatramaphanikumar; Kolli, Venkata Krishna Kishore (Applied intelligence (Dordrecht, Netherlands), 2022)
Target-specific sentiment classification has a dependency over the target term extraction. The majority of current studies in sentiment classification tasks do not utilize the complete linguistic and sentiment knowledge. Consequently, strenuous efforts are to be made for expressing the implications of each word from the sentences, which have significant amount of contextual dependencies. Hence, it leads to the problems like loss of semantics, missing of context-dependent information and also results in poor classification of the models. In this paper, we propose a Deep Finesse Network (DFN) to address these limitations and enhance the accuracy. The DFN employs a multichannel paradigm to exploit multi-grained sentiment features by leveraging the existing linguistic and sentiment knowledge more effectively without any human involvement. In each channel, the model firstly extracts the local features from the multi-grained sentiment features and then captures the global and spatial information of the identified local features. Secondly, it directly models the contextual relationships with enriched semantic information from the global features. Subsequently, the intra-sequence relations were also modeled among the contextual features to identify the target features in order to understand and predict the sentiments of identified contextual features. Finally, the effectiveness of the DFN is also evaluated on different datasets. The results proved that DFN outperforms all the current and advanced state-of-art models in classification accuracy in most cases.