Aspect-level sentiment analysis (ALSA) is the process of collecting, processing, analyzing, inferring, and synthesizing subjective sentiments of entities contained in texts at the aspect level. The ...development of social networks has been driven by the on-going appearance of vast numbers of short documents, such as those in which opinions are expressed and comments are made. The text in these documents reflects users’ emotions related to entities. The ALSA of these short texts plays an important role in solving various problems in life. Particularly in e-commerce, manufacturers can use sentiment analysis to determine users’ orientations, adapt their products to perfection, identify potential users, and pinpoint users that influence other users. Therefore, improving the performance of ALSA methods has recently attracted the interest of researchers. Currently, four main types of ALSA methods are available: knowledge-based, machine learning-based, hybrid-based, and most recently, graph convolutional network (GCN)-based. This study is the first survey to focus on reviewing the proposed methods for ALSA using GCN methods. In this paper, we propose a novel taxonomy to divide GCN-based ALSA models into three categories based on the types of knowledge extraction. We present and compare GCN-based ALSA methods following our taxonomy comprehensively. Common benchmark datasets and text representations that are often used in GCN-based methods are also discussed. In addition, we discuss five challenges and suggest seven future research directions for GCN-based ALSA methods. The findings of our survey are expected to provide the necessary guidelines for beginners, practitioners, and new researchers to improve the performance of ALSA methods.
•A concise overview of aspect-level sentiment analysis (ALSA) and GCNs.•Proposal of first taxonomy for GCNs-based aspect-level sentiment analysis methods.•A comprehensive review of modern GCNs methods for aspect-level sentiment analysis.•Five possible current challenges of GCNs-based aspect-level sentiment analysis.•Seven suggestions for future directions of GCNs-based ALSA.
A survey on sentiment analysis challenges Hussein, Doaa Mohey El-Din Mohamed
Journal of King Saud University. Engineering sciences,
October 2018, 2018-10-00, 2018-10-01, Letnik:
30, Številka:
4
Journal Article
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With accelerated evolution of the internet as websites, social networks, blogs, online portals, reviews, opinions, recommendations, ratings, and feedback are generated by writers. This writer ...generated sentiment content can be about books, people, hotels, products, research, events, etc. These sentiments become very beneficial for businesses, governments, and individuals. While this content is meant to be useful, a bulk of this writer generated content require using the text mining techniques and sentiment analysis. But there are several challenges facing the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting the suitable sentiment polarity. Sentiment analysis is the practice of applying natural language processing and text analysis techniques to identify and extract subjective information from text. This paper presents a survey on the sentiment analysis challenges relevant to their approaches and techniques.
The inception and rapid growth of the Web, social media, and other online forums have resulted in the continuous and rapid generation of opinionated textual data. Several real-world applications have ...been focusing on determining the sentiments expressed in these data. Owing to the multilinguistic nature of the generated data, there exists an increasing need to perform sentiment analysis on data in diverse languages. This study presents an overview of the methods used to perform sentiment analysis across languages. We primarily focus on multilingual and cross-lingual approaches. This survey covers the early approaches and current advancements that employ machine learning and deep learning models. We categorize these methods and techniques and provide new research directions. Our findings reveal that deep learning techniques have been widely used in both approaches and yield the best results. Additionally, the scarcity of multilingual annotated datasets limits the progress of multilingual and cross-lingual sentiment analyses, and therefore increases the complexity in comparing these techniques and determining the ones with the best performance.
Recently sentiment analysis in Arabic has attracted much attention from researchers. A modest number of studies have been conducted on Arabic sentiment analysis. However, due to the vast increase in ...users' comments and reviews on social media and e-commerce websites, the necessity to detect sentence-level and aspect-level sentiments has also increased. The aspect-based sentiment analysis has emerged to detect sentiments at the aspect level. Few studies have attempted to perform aspect-based sentiment analysis on Arabic texts because Arabic natural language processing is a challenging task and because of the lack of available Arabic annotated corpora. In this paper, we conducted a systematic review of the methods, techniques, and datasets employed in aspect-based sentiment analysis on Arabic texts. A total of 21 articles published between 2015-2021 were included in this review. After analysing these articles, we found a lack of annotated datasets that can be used by researchers. In addition, the used datasets were limited to few fields. This review will serve as a foundation for researchers interested in Aspect-Based Sentiment Analysis, it will assist them in developing new models and techniques to tackle this task in the future.
Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency ...tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN. To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.
Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk ...management, market research, and politics, to name a few. Given its saturation in specific subtasks - such as sentiment polarity classification - and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.
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