Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection have drawn a lot of attention in recent years due to a broad range of useful applications in various domains. The main goal of affect detection tasks is to recognizestatessuch as mood, sentiment, and emotions from textual data (e.g., news articles or product reviews). Despite the importance of utilizingpreprocessingsteps in different stages (i.e., word representation learning and building a classification model) of affect detection tasks, this topic has not been studied well. To that end, we explore whether applying various preprocessing methods (stemming, lemmatization, stopword removal, punctuation removal and so on) and their combinations in different stages of the affect detection pipeline can improve the model performance. The are many preprocessing approaches that can be utilized in affect detection tasks. However, their influence on the final performance depends on the type of preprocessing and the stages that they are applied. Moreover, the preprocessing impacts vary across different affective tasks. Our analysis provides thorough insights into how preprocessing steps can be applied in building an effect detection pipeline and their respective influence on performance.

Graph Neural Networks (GNNs) have been proposed to learn graph representations for various graph mining tasks such as link prediction and node classification. These methods aggregate information from neighbors of a node to generate the node representation vector. Temporal GNN models consider the temporal and neighborhood information of nodes. However, few temporal GNN methods consider network substructures such as triads and cliques. In this paper, we present a temporal GNN-based method that generates node embeddings by aggregating neighbors of a node that exist in the maximal cliques of the graph containing the node. The reason for considering neighbors that form a maximal clique with the target node is that nodes in a maximal clique are highly connected to each other and most likely share similar characteristics. In addition, we consider the time dependency of nodes by generating temporal walks on the cliques such that in these walks the time order of the nodes is respected. The node embedding is based on the aggregation of the node’s temporal walks. Our experiments on seven datasets show the effectiveness of our method in both link prediction and node classification tasks. Furthermore, our method is faster than other baselines we evaluate.

Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have been popular in recent years due to abundance of user-generated data, accurate computational linguistic models, and broad range of relevant applications in various domains.At the same time, many studies have highlighted the importance of text preprocessing, as an integral step to any natural language processing prediction model and downstream task.While preprocessing in affective systems is well-studied, preprocessing in word vector based models applied to affective systems, is not.To address this limitation, we conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models.Our analysis is the first of its kind and provides useful insights of the importance of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase.

Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets.Despite its usefulness, sarcasm detection remains a challenging task, due to a lack of any vocal intonation or facial gestures in textual data.To date, most of the approaches to addressing the problem have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec.However, these models inherit limitations that render them inadequate for the task of sarcasm detection.In this paper, we propose two novel deep neural network models for sarcasm detection, namely ACE 1 and ACE 2. Given as input a text passage, the models predict whether it is sarcastic (or not).Our models extend the architecture of BERT by incorporating both affective and contextual features.To the best of our knowledge, this is the first attempt to directly alter BERT's architecture and train it from scratch to build a sarcasm classifier.Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.