Abstract Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ...ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model’s interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications.
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Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward ...an entity. It has become a powerful tool used by governments, businesses, medicine, marketing, and others. The traditional sentiment analysis model focuses mainly on text content. However, technological advances have allowed people to express their opinions and feelings through audio, image and video channels. As a result, sentiment analysis is shifting from unimodality to multimodality. Multimodal sentiment analysis brings new opportunities with the rapid increase of sentiment analysis as complementary data streams enable improved and deeper sentiment detection which goes beyond text-based analysis. Audio and video channels are included in multimodal sentiment analysis in terms of broadness. People have been working on different approaches to improve sentiment analysis system performance by employing complex deep neural architectures. Recently, sentiment analysis has achieved significant success using the transformer-based model. This paper presents a comprehensive study of different sentiment analysis approaches, applications, challenges, and resources then concludes that it holds tremendous potential. The primary motivation of this survey is to highlight changing trends in the unimodality to multimodality for solving sentiment analysis tasks.
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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.
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4.
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, Volume:
30, Issue:
4
Journal Article
Peer reviewed
Open access
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.
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Humans are increasingly integrated with devices that enable the collection of vast unstructured opinionated data. Accurately analysing subjective information from this data is the task of sentiment ...analysis (an actively researched area in NLP). Deep learning provides a diverse selection of architectures to model sentiment analysis tasks and has surpassed other machine learning methods as the foremast approach for performing sentiment analysis tasks. Recent developments in deep learning architectures represent a shift away from Recurrent and Convolutional neural networks and the increasing adoption of Transformer language models. Utilising pre-trained Transformer language models to transfer knowledge to downstream tasks has been a breakthrough in NLP.
This survey applies a task-oriented taxonomy to recent trends in architectures with a focus on the theory, design and implementation. To the best of our knowledge, this is the only survey to cover state-of-the-art Transformer-based language models and their performance on the most widely used benchmark datasets. This survey paper provides a discussion of the open challenges in NLP and sentiment analysis. The survey covers five years from 1st July 2017 to 1st July 2022.
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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.
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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.
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Sentiment Analysis of Short Informal Texts Kiritchenko, S.; Zhu, X.; Mohammad, S. M.
The Journal of artificial intelligence research,
08/2014, Volume:
50
Journal Article
Peer reviewed
Open access
We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word ...or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words.
The system ranked first in the SemEval-2013 shared task `Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.
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