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This paper presents a flexible and automated methodology for extracting and analyzing customer sentiment in the restaurant industry through online reviews. The proposed approach is evaluated on a sample dataset of 1000 reviews, as well as applied within an accompanying web application that utilizes a large corpus of 880,000 reviews from 1581 restaurants located in the Algarve region. By leveraging advanced Natural Language Processing (NLP) techniques such as Aspect-Based Sentiment Analysis (ABSA), this study seeks to accurately classify customer sentiments according to specific attributes related to food quality, service, ambiance, pricing and location. To assess its performance against human classification processes, the results demonstrate that the proposed methodology effectively replicates them with three alternative approaches for attribute extraction and classification being presented; among which BART model consistently outperforms DeBERTa while ChatGPT achieves highest F1 Score. Named RestMON Algarve, the developed web application will allow restaurant managers to extract and analyze customer sentiment from online reviews; track attribute evolution over time; compare performance between competing restaurants - thus providing relevant insights into enhancing customer satisfaction levels leading towards overall success in hospitality industry. [ABSTRACT FROM AUTHOR]

In contrast to earlier ABSA studies primarily concentrating on individual sentiment components, recent research has ventured into more complex ABSA tasks encompassing multiple elements, including pair, triplet, and quadruple sentiment analysis. Quadruple sentiment analysis, also called aspect-category-opinion-sentiment quadruple Extraction (ACOSQE), aims to dissect aspect terms, aspect categories, opinion terms, and sentiment polarities while considering implicit sentiment within sentences. Nonetheless, a comprehensive overview of ACOSQE and its corresponding solutions is currently lacking. This is the precise gap that our survey seeks to address. To be more precise, we systematically reclassify all subtasks of ABSA, reorganizing existing research from the perspective of the involved sentiment elements, with a primary focus on the latest advancements in the ACOSQE task. Regarding solutions, our survey offers a comprehensive summary of the state-of-the-art utilization of language models within the ACOSQE task. Additionally, we explore the application of ChatGPT in sentiment analysis. Finally, we review emerging trends and discuss the challenges, providing insights into potential future directions for ACOSQE within the broader context of ABSA. [ABSTRACT FROM AUTHOR]

Target Aspect Sentiment Detection (TASD) is challenging because it involves various Natural Language Processing (NLP) subtasks including opinion target detection and sentiment polarity classification. Despite significant advancements in this area, most studies have neglected the interrelation between opinion elements and contexts, primarily when a target opinion is expressed implicitly. This study proposes Aspect-Target-Context-Aware Detection for Target Aspect Sentiment Detection, which is a joint learning neural-based framework. The Aspect-Target-Context-Aware Detection model incorporates opinion context syntactic information by utilizing dependency relations associated with opinion terms, and considers head nodes as a primary element for identifying relevant opinion contexts. The Target Aspect Sentiment Detection task was divided into aspect sentiment classification and opinion target extraction tasks. For aspect sentiment, multiclass classification was employed for aspect-sentiment pairs. A BIO tag inference scheme is adopted to detect the opinion target and determine its type (implicit or explicit) for opinion target extraction. The approach was evaluated using two restaurant datasets: Task-5 of SemEval-2016 and Task-12 of SemEval-2015. The proposed approach demonstrated cutting-edge performance when extracting multi-opinion elements from the TASD task, with notable improvements in Macro-F1 values: 3.28% for SemEval 2015 and 5.97% for SemEval 2016. The model also identifies various opinion types and offers valuable insights for future developments, particularly for implicit opinion detection.

Sentiment analysis is essential for comprehending public opinion, particularly when considering e-commerce and the expansion of online businesses. Early approaches treated sentiment analysis as a document or sentence-level classification problem, lacking the ability to capture nuanced opinions about specific aspects. This limitation was addressed by the development of aspect-based sentiment analysis (ABSA), which links sentiment to specific aspects that are mentioned explicitly or implicitly in the review. ABSA is relatively a recent field of sentiment analysis and the existing models for ABSA face three main challenges, including domain-specificity, reliance on labeled data, and a lack of exploration into the potential of newer large language models (LLMs) such as GPT, PaLM, and T5. Leveraging a diverse set of datasets, including DOTSA, MAMS, and SemEval16, we evaluate the performance of prominent models such as ATAE-LSTM, flan-t5-large-absa, DeBERTa, PaLM, and GPT-3.5-Turbo. Our findings reveal nuanced strengths and weaknesses of these models across different domains, with DeBERTa emerging as consistently high-performing and PaLM demonstrating remarkable competitiveness for aspect term sentiment analysis (ATSA) tasks. In addition, the PaLM demonstrates competitive performance for all the domains that were used in the experiments including the restaurant, hotel, books, clothing, and laptop reviews. Notably, the analysis underscores the models’ domain sensitivity, shedding light on their varying efficacy for both ATSA and ACSA tasks. These insights contribute to a deeper understanding of model applicability and highlight potential areas for improvement in ABSA research and development.

There are quite a few comment sentences without emotional words in aspect-level emotional texts, and the study of their emotions is called aspect-level implicit sentiment analysis. The existing models have the problems that the context information related to aspect words may be lost in the pre-training process, and the deep features in the context cannot be accurately extracted. Aiming at the first problem, this paper constructs an aspect-aware BERT pre-training model, and introduces aspect words into the input embedding structure of basic BERT to generate word vectors related to aspect words. Aiming at the second problem, this paper constructs a context-aware attention mechanism. For the deep hidden vectors obtained from the coding layer, the semantic and syntactic information is introduced into the attention weight calculation process, so that the attention mechanism can more accurately assign weights to the context related to aspect words. The results of comparative experiments show that the proposed model outperforms the baseline model. BERT [ABSTRACT FROM AUTHOR]

As technology advances, Facebook, Twitter, and microblogging sites have become the most effective platforms for communication and information exchange. Through these forums, people can share their views and experiences. These platforms enable discussion about a certain product that can be a valuable resource used to inform any decision-making process. For such studies, the majority of advanced-level researchers employed deep learning and machine learning models in conjunction with natural language processing (NLP). In recent years, the use of pre-trained models, such as Glove and BERT, in aspect-based sentiment analysis (ABSA) has increased. In ABSA, the auxiliary information is required to distinguish each aspect of this fine-grained task. However, BERT’s input format is restricted to a collection of words that cannot include more context knowledge. To address this problem, a BiLSTM and embedded CNN-based deep learning model has been presented for sentiment analysis at the aspect level. Initially, datasets were compiled from several sources. Then, an auxiliary feature was extracted using standard NLP. The auxiliary features were further refined and transformed into feature vectors based on the proposed embedded CNN model. Finally, a BiLSTM-based classification has been performed for sentiment classification. The experimental evaluation demonstrated that the performance of the suggested technique achieves on the SemEval dataset in terms of F1 score and accuracy by 81.7 and 83.3 percentage points, respectively, and on the product review dataset by 80.8 and 83.1 percentage points, respectively.

As technology advances, Facebook, Twitter, and microblogging sites have become the most effective platforms for communication and information exchange. Through these forums, people can share their views and experiences. These platforms enable discussion about a certain product that can be a valuable resource used to inform any decision-making process. For such studies, the majority of advanced-level researchers employed deep learning and machine learning models in conjunction with natural language processing (NLP). In recent years, the use of pre-trained models, such as Glove and BERT, in aspect-based sentiment analysis (ABSA) has increased. In ABSA, the auxiliary information is required to distinguish each aspect of this fine-grained task. However, BERT's input format is restricted to a collection of words that cannot include more context knowledge. To address this problem, a BiLSTM and embedded CNN-based deep learning model has been presented for sentiment analysis at the aspect level. Initially, datasets were compiled from several sources. Then, an auxiliary feature was extracted using standard NLP. The auxiliary features were further refined and transformed into feature vectors based on the proposed embedded CNN model. Finally, a BiLSTM-based classification has been performed for sentiment classification. The experimental evaluation demonstrated that the performance of the suggested technique achieves on the SemEval dataset in terms of F1 score and accuracy by 81.7 and 83.3 percentage points, respectively, and on the product review dataset by 80.8 and 83.1 percentage points, respectively. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis has been a popular topic in natural language processing in recent years that aims to determine the sentiment polarity of a specific aspect in one context. However, most existing models only focus on feature extraction and ignore the significant role of words with sentiment tendency (e.g. good, terrible), which results in low classification accuracy. In this paper, a sentiment knowledge-based bidirectional encoder representation from transformers (SK-BERT) is proposed to overcome this shortcoming. To introduce sentiment knowledge, SK-BERT first integrates sentiment knowledge words into independent sequences and then encodes the sequence and context into static and dynamic vectors with the BERT pretrained models, respectively. All vectors are sent to the sentiment centre to generate different dimension representations for classification. We evaluate our model on three widely used datasets. Experimental results show that the proposed SK-BERT model outperforms other state-of-the-art models. Furthermore, visualization experiments are implemented to prove the rationality of SK-BERT. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) of patients' opinions expressed in drug reviews can extract valuable information about specific aspects of a particular drug such as effectiveness, side effects and patient conditions. One of the most important and challenging tasks of ABSA is to extract the implicit and explicit aspects from a text, and to classify the extracted aspects into predetermined classes. Supervised learning algorithms possess high accuracy in extracting and classifying aspects; however, they require annotated datasets whose manual construction is time-consuming and costly. In this paper, first a new method was introduced for identifying expressions that indicate an aspect in user reviews about drugs in English. Then, distant supervision was adopted to automate the construction of a training set using sentences and phrases that are annotated as aspect classes in the drug domain. The results of the experiments showed that the proposed method is able to identify various aspects of the test set with 74.4% F-measure, and outperforms the existing aspect extraction methods. Also, training the random forest classifier on the dataset that was constructed via distant supervision obtained the F-measure of 73.96%, and employing this dataset to fine-tune BERT for aspect classification yielded better F-measure (78.05%) in comparison to an existing method in which the random forest classifier trained on an accurate manually constructed dataset. [ABSTRACT FROM AUTHOR]

Aspect-based Sentiment analysis (ABSA) accomplishes a fine-grained analysis that defines the aspects of a given document or sentence and the sentiments conveyed regarding each aspect. This level of analysis is the most detailed version that is capable of exploring the nuanced viewpoints of the reviews. The bulk of study in ABSA focuses on English with very little work available in Arabic. Most previous work in Arabic has been based on regular methods of machine learning that mainly depends on a group of rare resources and tools for analyzing and processing Arabic content such as lexicons, but the lack of those resources presents another challenge. In order to address these challenges, Deep Learning (DL)-based methods are proposed using two models based on Gated Recurrent Units (GRU) neural networks for ABSA. The first is a DL model that takes advantage of word and character representations by combining bidirectional GRU, Convolutional Neural Network (CNN), and Conditional Random Field (CRF) making up the (BGRU-CNN-CRF) model to extract the main opinionated aspects (OTE). The second is an interactive attention network based on bidirectional GRU (IAN-BGRU) to identify sentiment polarity toward extracted aspects. We evaluated our models using the benchmarked Arabic hotel reviews dataset. The results indicate that the proposed methods are better than baseline research on both tasks having 39.7% enhancement in F1-score for opinion target extraction (T2) and 7.58% in accuracy for aspect-based sentiment polarity classification (T3). Achieving F1 score of 70.67% for T2, and accuracy of 83.98% for T3. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) aims to use interactions between aspect terms and their contexts to predict sentiment polarity for given aspects in sentences. Current mainstream approaches use deep neural networks (DNNs) combined with additional linguistic information to improve performance. DNN-based methods, however, lack explanation and transparency to support predictions, and no existing model completely solves the trade-off between explainability and performance. In contrast, most previous studies explain the relationship between input and output by attribution; however, this approach is insufficient to mine hidden semantics from abstract features. To overcome the aforementioned limitations, we propose a disentangled linguistic graph model (DLGM) to enhance transparency and performance by guiding the signal flow. First, we propose a disentangled linguistic representation learning module that extracts a specific linguistic property via neurons to help capture finer feature representations. To further boost explainability, we propose a supervised disentangling module, in which labeled linguistic data help reduce information redundancy. Finally, a cross-linguistic routing mechanism is introduced into the signal propagation of linguistic chunks to overcome the defect of distilling information in an intralinguistic property. Quantitative and qualitative experiments verify the effectiveness and superiority of the proposed DLGM in sentiment polarity classification and explainability. [ABSTRACT FROM AUTHOR]

Resilience has been intensely investigated as the viable quality of individuals, groups, organizations, and systems to respond productively to notable change without engaging in an extended period of regressive behaviour. Recently, there has been growing attention to the relationship between resilience and cities. To contribute to this stimulating debate, this paper first provides the theoretical framework and links the concept of resilience to urban studies. Subsequently, it enlightens, through a systems perspective and the aspect-based sentiment analysis (ABSA) methodology, the possibility to enrich the information variety endowment of urban policymakers, generated by new information units, to foster resilience capabilities in the urban context. Specifically, a large-scale text analysis study was conducted on the city of Rome to understand the sentiments expressed within the text generated online by citizens and visitors. The positive or negative sentiments linked to the hidden problems of the urban context were organized within collective perception-based maps for each of the analysed points of interest (POIs). Since cities represent complex decision-making contexts, this study aimed to outline a methodology and a tool that would help foster resilient thinking in urban policies by enriching the diversity of the information variety endowment of urban decision-makers. Unlabelled Image • Aiming to understanding what makes an urban context resilient. • The Aspect-Based Sentiment Analysis is applied for the first time to urban context. • The case of Rome in terms of vulnerabilities and resilience is analysed by ABSA methodology. • A useful urban management tool is proposed: the collective perception-based maps. • Implications for the urban management are discussed. [ABSTRACT FROM AUTHOR]