User Representation Learning for Social Networks: An Empirical Study

Abstract

Gathering useful insights from social media data has gained great interest over the recent years. User representation can be a key task in mining publicly available user-generated rich content offered by the social media platforms. The way to automatically create meaningful observations about users of a social network is to obtain real-valued vectors for the users with user embedding representation learning models. In this study, we presented one of the most comprehensive studies in the literature in terms of learning high-quality social media user representations by leveraging state-of-the-art text representation approaches. We proposed a novel doc2vec-based representation method, which can encode both textual and non-textual information of a social media user into a low dimensional vector. In addition, various experiments were performed for investigating the performance of text representation techniques and concepts including word2vec, doc2vec, Glove, NumberBatch, FastText, BERT, ELMO, and TF-IDF. We also shared a new social media dataset comprising data from 500 manually selected Twitter users of five predefined groups. The dataset contains different activity data such as comment, retweet, like, location, as well as the actual tweets composed by the users.

Aspect based Sentiment & Emotion Analysis with ROBERTa, LSTM

“…According to Fig. 8, anger (1,00,445) is the biggest feeling on Twitter associated with the Ukraine-Russia war, followed by optimism (39,284), joy (25,091), and sadness (25,795). Fig.…”

“…In [24], the authors use feedback to improve the RNN architecture and create the LTSM model. Schuster and Paliwal utilized the "Bi-LSTM" model [25]. Deep learning methods automatically identify text emotion features.…”