Emoji recommendation is an important task to help users find appropriate emojis from thousands of candidates based on a short tweet text. Traditional emoji recommendation methods lack personalized recommendation and ignore user historical information in selecting emojis. In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. In text encoder, a BERT model is contained to learn dense and low-dimensional representations of tweets. In personalized attention, user dynamic preferences are learned according to semantic and sentimental similarity between historical tweets and the tweet which is waiting for emoji recommendation. Informative historical tweets are selected and highlighted. Experiments are carried out on two real-world datasets from Sina Weibo and Twitter. Experimental results validate the superiority of our approach on personalized emoji recommendation.

PERD: Personalized Emoji Recommendation with Dynamic User Preference

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely “Azithromycin” in conjugation with Buprenorphine has Type ‘A’ side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions.

Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques

A self-attention hybrid emoji prediction model for code-mixed language: (Hinglish)

Dynamic pricing for fast charging stations with deep reinforcement learning

The shortage of information in text generation has been a prominent area of research in Natural Language Processing (NLP). Current research endeavors aim to combine pre-trained models with rich open-world knowledge from external sources to increase the priori information and thereby enhance the informativeness of text generation. While recent studies suggest that integrating open-world and task-specific knowledge can improve text generation by addressing specific knowledge gaps in downstream tasks, the inherent semantic ambiguity in natural language remains a significant challenge that may impede knowledge acquisition and text generation. To overcome this challenge and improve the model's semantic comprehension and overall robustness, we propose a novel framework, the Knowledge Augmentation Text Generation model via Adversarial Training (KTGAT). Our method adds perturbations to the embedding layer, which are equivalent to constructing unstable samples. This approach improves the model's robustness to adversarial samples and the generalization performance of the original samples. Our experiments demonstrate that the proposed KTGAT framework outperforms the baseline model, thus proving its effectiveness in improving text generation. The generated text cases illustrate that our method enhances the model's semantic comprehension and enables it to search for knowledge items more effectively and accurately.

KTGAT: Improving the Robustness of Knowledge-enhanced Text Generation via Adversarial Training

Sarcasm detection remains a challenge for numerous Natural Language Processing (NLP) tasks, such as sentiment classification or stance prediction. Existing sarcasm detection studies attempt to capture the subtle semantic incongruity patterns by using contextual information and graph information through Graph Convolutional Networks (GCN). However, direct application of dependence may inevitably introduce noisy information and inferiorly in modeling long-distance or disconnected words in the dependency tree. To better learn the sentiment inconsistencies between terms, we propose an Affection Enhanced Relational Graph Attention network (ARGAT) by jointly considering the affective information and the dependency information. Specifically, we use Relational Graph Attention Networks (RGAT) to integrate relation information guided by a trainable matrix of relation types and synchronously use GCNs to integrate affection information explicitly donated by affective adjacency matrixes. The employment of RGAT contributes to information interaction of structural relevant word pairs with a long distance. With the enhancement of affective information, the proposed model can capture complex forms of sarcastic expressions. Experimental results on six benchmark datasets show that our proposed approach outperforms state-of-the-art sarcasm detection methods. The best-improved results of accuracy and F1 are 4.19% and 4.33%, respectively.

https://www.mdpi.com/2076-3417/12/7/3639/pdf?version=1649408297

Affection Enhanced Relational Graph Attention Network for Sarcasm Detection

Sarcasm is widely used in social communities and e-commerce platforms, failing to detect it in natural language processing tasks leads to false positives, e.g., opinion mining and sentiment classification. Recent works have indicated that the two linguistic characteristics, sentiment and incongruity information are beneficial to sarcasm detection. However, sarcasm datasets with sentiment labels are usually unavailable, and researchers consider little semantic information while modeling incongruity. In this paper, we propose a multi-task learning framework that incorporates sentiment clues by soft sentiment labels and integrates semantic information while modeling context incongruity. Experimental results on datasets show that the model we proposed yields better performance for the sarcasm detection task with the help of sentiment clues and incongruity information.

Jointly Learning Sentimental Clues and Context Incongruity for Sarcasm Detection

The rapid development of social media makes it easy for people to acquire information while also providing a platform for publishing and spreading fake news. Fake news brings plenty of explicit and implicit risks to social stability, making fake news detection an issue that deserves attention. Recent methods based on graph neural networks (GNN) achieve impressive results in fake news detection, but their performance is still limited in practice due to the absence of high-order relations between nodes. In this paper, we propose a Sentiment-Aware Hypergraph Attention Network (SA-HyperGAT) for fake news detection. SA-HyperGAT can better leverage different kinds of information from news contents and user comments with hypergraphs, which can capture higher-order dependency between words and sentences compared with general graphs. Specifically, we first construct two hypergraphs with distinct types of nodes and hyperedges to utilize structural information of news contents and sentimental information of user comments. Then we adopt a hypergraph attention network with a dual attention mechanism to learn the composed representations of two hypergraphs for the final prediction. Our proposed SA-HyperGAT outperforms competitive baselines on two real-world datasets. Extensive experimental results prove the effectiveness of each component in SA-HyperGAT.

Sentiment-Aware Fake News Detection on Social Media with Hypergraph Attention Networks

Due to the booming popularity of online social networks, emojis have been widely used in online communication. As nonverbal language units, emojis help to convey emotions and express feelings. In this article, we focus on the sentiment-aware emoji insertion task, which predicts multiple emojis and their positions in a sentence conditioned on the plain texts and sentiment polarities. To facilitate future research in this field, we construct a large-scale emoji insertion corpus named “MultiEmoji,” which contains 420 000 English posts with at least one emoji per post. We formulate the insertion process as a sequence tagging task and apply a BERT-BiLSTM-CRF model to the insertion of emojis. Extensive experiments illustrate that our model outperforms existing methods by a large margin.

Sentiment-Aware Emoji Insertion Via Sequence Tagging

Sarcasm is a sophisticated expression, commonly used on social media, e.g., Reddit and Twitter. The presence of sarcasm in social media text flips the polarity of sentiment, thus hindering the performance of works that require true sentiment, e.g., sentiment analysis and opinion mining. However, current works fail to exploit commonsense knowledge in the sarcasm detection task. In this paper, we revisit sarcasm detection from a novel perspective, which models commonsense knowledge as well as context semantics to reason with sarcasm. More specifically, we propose a commonsense-aware model with a heterogeneous graph attention network that leverages commonsense knowledge, enabling it to better understand implied sentiment behind the literal meaning. We conduct experiments on benchmark datasets from Reddit and Internet Argument Corpus. Experimental results show that our proposed approach yields superior performance with commonsense knowledge integrated.

Bo Liu

Papers

Affection Enhanced Relational Graph Attention Network for Sarcasm Detection

Jointly Learning Sentimental Clues and Context Incongruity for Sarcasm Detection

Sentiment-Aware Fake News Detection on Social Media with Hypergraph Attention Networks

Sentiment-Aware Emoji Insertion Via Sequence Tagging

Commonsense-Aware Sarcasm Detection with Heterogeneous Graph Attention Network