International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 1, February 2024, pp. 690~697 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp690-697  690 Journal homepage: http://ijece.iaescore.com Graph embedding approach to analyze sentiments on cryptocurrency Ihab Moudhich, Abdelhadi Fennan List Laboratory, Faculty of Sciences and Techniques, University Abdelmalek Essaadi, Tangier, Morocco Article Info ABSTRACT Article history: Received Apr 19, 2023 Revised Jul 8, 2023 Accepted Jul 17, 2023 This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data. Keywords: Graph embedding Machine learning Natural language processing Sentiment analysis Word embedding Word2vec This is an open access article under the CC BY-SA license. Corresponding Author: Ihab Moudhich List Laboratory, Faculty of Sciences and Techniques, University Abdelmalek Essaadi Tangier, Morocco Email: ihab.moudhich@gmail.com 1. INTRODUCTION Sentiment analysis [1] is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions. In recent years, sentiment analysis has gained significant attention from researchers and practitioners alike due to its wide range of applications, including social media monitoring [2], customer feedback analysis [3], and market research [4]. To extract insights from text data at scale, natural language processing (NLP) techniques [5] have become essential in the sentiment analysis field. Sentiment analysis has become an essential tool for businesses and individuals alike to analyze social media [6] content and gain insights into public opinion and sentiment. Social media platforms, such as Twitter, Facebook, and Instagram, generate massive amounts of user-generated content on a daily basis, including posts, comments, and tweets. Sentiment analysis enables users to process and analyze this data to gain insights into how users are feeling about a particular topic, product, or brand. Social media monitoring is one of the most common applications of sentiment analysis. By analyzing social media content, businesses can gain valuable insights into customer sentiment and