Attentional Encoder Network for Targeted Sentiment Classification

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.Comment: 7 page

Context-Preserving Sentiment Classification Using Bi-TCN And BI-GRU with Multi-Head Self-Attention

In natural language processing, sentiment classification is the recently used topic. Specifically, the objective of the sentiment analysis is to categorise the polarity expressed on the sentence's target. However, there are some researches for classifying the polarity of the target which outperforms well in their way. Yet, there are some limitations, such as apparent and in-apparent issues, gradient problems, etc., to overcome these issues the context-preserving sentiment classification using BI-TCN (Bidirectional Temporal Convolutional network) and BI-GRU (Bidirectional Gated Recurrent Unit) with Multi-head self-attention is proposed to extracts both the local dependent and global dependent information from the sentence, then it will incrementally extract the supervision information of the target to train the model. Formerly, the model is tested and trained using four datasets and the performance is compared with four existing methods, its accuracy is evaluated using the F1-score, precision, recall, specificity, and MCC (Matthews Correlation Coefficient). Consequently, the proposed approach provides the best accuracy level of  98%.

Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation