Abstract
Currently, expressing feelings through social media requires great consideration as an essential part of our lives; besides sharing ideas and thoughts, we share moments and good memories. Social media such as Facebook, Twitter, Weibo, and LinkedIn, are considered rich sources of opinionated text data. Both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. Most studies on social media analysis mainly classified sentiment as positive, negative, or neutral classes. The challenge in emotion analysis arises because humans can express one or several emotions within one expression. Human beings can recognize these different emotions well; however, it is still not easy for an emotion analysis system. In most cases, the Arabic language used through social media is of a slangy or colloquial form, making it more challenging to preprocess and filter noise since most lemmatization and stemming tools are built on Modern Standard Arabic (MSA). An emotion analysis model has been implemented to categorize emotions. The model is a multiclass and multilabel classification problem. However, few studies have been adapted for this emotion classification problem in Arabic social media. Nearly the only work is the one of SemEval 2018 task1- sub-task E-c. Several machine learning approaches have been implemented in this task; a few studies were based on deep learning. Our model implemented a novel multilayer bidirectional long short term memory (BiLSTM) trained on top of pre-trained word embedding vectors. The model achieved state-of-the-art performance enhancement. This approach has been compared with other models developed in the same tasks using Support Vector Machines (SVM), random forest (RF), and fully connected neural networks. The proposed model achieved a performance improvement over the best results obtained for this task.
Highlights
With the rapid growth of web applications, such as E-commerce platforms and substantial social media comments in various fields, an urgent need to deal with this massive amount of web data and automatically extract helpful information has arisen
We investigated the effect of changing the number of bidirectional long short term memory (BiLSTM) layers to improve the performance
Our proposed approach is the first deep learning model based on BiLSTM for Arabic social media multi label emotion classification
Summary
With the rapid growth of web applications, such as E-commerce platforms and substantial social media comments in various fields, an urgent need to deal with this massive amount of web data and automatically extract helpful information has arisen. Sentiment analysis models play a significant role in this task. Sentiment analysis is a computational field within natural language processing (NLP) concerned with people’s sentiments and opinions toward objects such as services, persons, products, events, organizations, Khalil et al J Big Data (2021) 8:136 and topics. Thanks to the availability of high-performance computational computers, which allows using different machine learning techniques, especially deep learning, to build high-performance, robust automatic sentiment analysis models. Neutral, or negative opinions from the text. Emotion analysis is one of the most common sentiment analysis tasks for recognizing different feelings through text expression
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
