We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.

SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval).

Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.

Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Mining the emotions in the text related to mental health-care oriented is a challenging aspect, especially dealing with a long-text sequence of data. The extraction of emotions depends upon the various psychological depression factors like negative and ambiguity. Identifying these factors is the most perplexing task for every psychiatrist to treat their patients. Our study includes the deep learning (DL) models with global vector representations (GloVe) embeddings to capture the text sequence of data. We proposed a model multi-head attention with bidirectional long short-term memory and convolutional neural network (MHA-BCNN) is a pre-eminent mechanism that outperforms better than past research works for capturing the negative text-based emotions. In this paper, by using DL extracted the various negative mental-health emotions like addiction, anxiety, depression, insomnia, stress, and obsessive cleaning disorder (OCD). By using the GloVe embeddings and handled the ambiguity factors like multiple emotion words in a certain sequence. As we proposed a vigorous appliance in our research to capture and hoard the long-term dependencies. We extracted the questions related to mental health issues were posted by the patients in an online mental healthcare-oriented platform. We efficaciously handled both negative and ambiguity factors at the document level. Our suggested exemplary MHA-BCNN surmounts various aspects from preceding research works and ensued preeminent performance. Experimental results show that our proposed framework MHA-BCNN outperformed than the erstwhile research works.

Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model

An emotional classification method of Chinese short comment text based on ELECTRA

Aspect-level sentiment classification is a hot research topic in natural language processing (NLP). One of the key challenges is that how to develop effective algorithms to model the relationships between aspects and opinion words appeared in a sentence. Among the various methods proposed in the literature, the graph convolutional networks (GCNs) achieve the promising results due to their good ability to capture the long distance between the aspects and the opinion words. However, the existing methods cannot effectively leverage the edge information of dependency parsing tree, resulting in the sub-optimal results. In this article, we propose a syntactic edge-enhanced graph convolutional network (ASEGCN) for aspect-level sentiment classification with interactive attention. Our proposed method can effectively learn better representations of aspects and the opinion words by considering the different types of neighborhoods with the edge constraint. To evaluate the effectiveness of our proposed method, we conduct the experiments on five standard sentiment classification results. Our results demonstrate that our proposed method obtains the better performance than the state-of-the-art models on four datasets, and achieves a comparative performance on Rest16.

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Syntactic Edge-Enhanced Graph Convolutional Networks for Aspect-Level Sentiment Classification With Interactive Attention

This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network &#x0028 CMA-MemNet &#x0029  This is an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects In order to fix the memory network&#x02BC s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself Meanwhile, unlike most recurrent neural network &#x0028 RNN &#x0029 long short term memory &#x0028 LSTM &#x0029 , gated recurrent unit &#x0028 GRU &#x0029 models, we retain the parallelism of the network We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6 Compared with some popular baseline methods, our model performs excellently

Convolutional multi-head self-attention on memory for aspect sentiment classification

Offensive language has become pervasive in social media. In Offensive Language Identification tasks, it may be difficult to predict accurately only according to the surface words. So we try to dig deeper semantic information of text. This paper presents use an attention-based two layers bidirectional longshort memory neural network (BiLSTM) for semantic feature extraction. Additionally, a residual connection mechanism is used to synthesize two different deep features, and an emoji attention mechanism is used to extract semantic information of emojis in text. We participated in three sub-tasks of SemEval 2019 Task 6 as CN-HIT-MI.T team. Our macro-averaged F1-score in sub-task A is 0.768, ranking 28/103. We got 0.638 in sub-task B, ranking 30/75. In sub-task C, we got 0.549, ranking 22/65. We also tried some other methods of not submitting results.

Tiejun Zhao

Papers

Convolutional multi-head self-attention on memory for aspect sentiment classification

CN-HIT-MI.T at SemEval-2019 Task 6: Offensive Language Identification Based on BiLSTM with Double Attention