Sentiment classification using attention mechanism and bidirectional long short-term memory network

Japanese waka translation supported by internet of things and artificial intelligence technology

With the advancement of internet of things (IoT) and artificial intelligence (AI) technology, access to large-scale bilingual parallel data has become more efficient, thereby accelerating the development and application of machine translation. Given the increasing cultural exchanges between China and Japan, many scholars have begun to study the Chinese translation of Japanese waka poetry. Based on this, the study first explores the structure of waka and the current state of its Chinese translations, analyzing existing translation disputes and introducing a data collection method for waka using IoT. Then, an optimized neural machine translation model is proposed, which integrates a Bidirectional Long Short-Term Memory (Bi-LSTM) network, vertical Tree-LSTM, and an attention mechanism into the Transformer framework. Experimental results demonstrate that the three optimized models-Transformer + Bi-LSTM, Transformer + Tree-LSTM, and Transformer + Tree-LSTM + Attention-outperform the baseline Transformer model on both public and waka datasets. The BLEU scores of the models on the public dataset were 23.71, 23.95, and 24.12, respectively. Notably, on the waka dataset, the Transformer + Tree-LSTM + Attention model achieved the highest BLEU score of 20.65, demonstrating a significant advantage in capturing waka's unique features and contextual information. This study offers new methods to enhance the quality of Chinese-Japanese translation, promoting cultural exchange and understanding.

An Attention-Based Multi-Representational Fusion Method for Social-Media-Based Text Classification

There exist various text-classification tasks using user-generated contents (UGC) on social media in the big data era. In view of advantages and disadvantages of feature-engineering-based machine-learning models and deep-learning models, we argue that fusing handcrafted-text representation via feature engineering and data-driven deep-text representations extracted by performing deep-learning methods is conducive to enhancing text-classification capability. Given the characteristics of different deep neural networks, their complementary effect needs to be investigated. Moreover, contributions of these representations need to be adaptively learned when it comes to addressing different tasks or predicting different samples. Therefore, in this paper, we propose a novel fused deep-neural-network architecture with a hierarchical attention mechanism for text classification with social media data. Specifically, in the context that handcraft features are available, we employ the attention mechanism to adaptively fuse totally data-driven-text representation and handcrafted representation. For the generation of the data-driven-text representation, we propose a data-driven encoder that fuses text representations derived from three deep-learning methods with the attention mechanism, to adaptively select discriminative representation and explore their complementary effect. To verify the effectiveness of our approach, we performed two text-classification tasks, i.e., identifying adverse drug reaction (ADR)-relevant tweets from social media and identifying comparative-relevant reviews from an E-commerce platform. Experimental results demonstrate that our approach outperforms other baselines.