Discovering Hate Sentiment within Twitter Data through Aspect-Based Sentiment Analysis

• Main Authors: Ibrahim, R. (Author), Selamat, A. (Author), Zainuddin, N. (Author)
• Format: Article
• Language: English
• Published: Institute of Physics Publishing, 2020
• Subjects: Automatic prediction; Contextualisation; Deep learning; Deep neural networks; Embeddings; Feature representation; Learning algorithms; Learning systems; Machine learning models; Machine learning techniques; NAtural language processing; Semantic properties; Semantics; Sentiment analysis; Social networking (online); Vector representations
• Online Access: View Fulltext in Publisher; View in Scopus

 Aspect-based sentiment analysis is a vital issue in fine-grained sentiment evaluation, which intends to provide an automatic prediction of the sentiment polarity, given a particular aspect in its context. This paper presents an aspect-based sentiment analysis to find hate sentiment inside twitter data. Word embeddings have had prevalent utilisation in Natural Language Processing (NLP) applications because their vector representations have the ability to capture useful linguistic relationships and semantic properties between words with the help of deep neural networks. Word embeddings have often been used in machine learning models as feature input, which allows for the contextualisation of raw text data in machine learning techniques. The model has the ability to represent the relationship between the word embedding features and the aspects as feature representation within the suggested model. To assess the efficacy of the proposed method, extensive experiments were performed on the dataset of the researcher, as well as on widely utilised datasets. It was demonstrated by the experimental results that the proposed method was able to obtain impressive results among the three datasets. © Published under licence by IOP Publishing Ltd.