Open AccessDiscovering Hate Sentiment within Twitter Data through Aspect-Based Sentiment Analysis
Author(s) -
Nurulhuda Zainuddin,
Ali Selamat,
Roliana Ibrahim
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1447/1/012056
Subject(s) - sentiment analysis , computer science , word embedding , artificial intelligence , natural language processing , word (group theory) , feature (linguistics) , representation (politics) , context (archaeology) , deep learning , embedding , machine learning , linguistics , paleontology , philosophy , politics , political science , law , biology
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.