Fine-grained Sentiment Analysis Research Articles

Improving span-based Aspect Sentiment Triplet Extraction with part-of-speech filtering and contrastive learning

Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of aspects and opinions as candidate spans that contain many invalid and irrelevant spans. This made the model training and prediction more difficult due to noised spans, leading to poor performance. To address this issue, we first propose a novel span-level approach that explicitly considers prior grammatical knowledge to generate possible candidate spans by part-of-speech filtering. In this way, our approach can make the model easier to be trained and achieve higher performance at the test stage. Besides, the quality of span-level representation of aspects and opinions is crucial for predicting their sentiment relation. To build a high-quality span-level representation of aspects and opinions, we first incorporate the contextual embedding of the entire sequence into span-level representations. Then, we introduce an auxiliary loss based on contrastive learning to make a more compact representation of the same polarities. Experimental evaluations on the 14Lap, 14Res, 15Res, and 16Res datasets demonstrate the effectiveness of our model, achieving state-of-the-art performance in span-based triplet extraction.

Enhancing aspect-based sentiment analysis with BERT-driven context generation and quality filtering

Fine-grained sentiment analysis, commonly referred to as aspect-based sentiment analysis (ABSA), has garnered substantial attention in both academic and industrial circles. ABSA focuses on unveiling the sentiment orientation associated with specific entities or attributes within textual data, resulting in a more precise depiction of intricate emotional nuances. However, due to the extensive range of applications for ABSA, certain domains face challenges such as constrained dataset sizes and the absence of exhaustive, high-quality corpora, leading to issues like few-shot learning and resource scarcity scenarios. To address the issue of limited training dataset sizes, one viable approach involves the utilization of text-based context generation to expand the dataset. In this study, we amalgamate Bert-based text generation with text filtering algorithms to formulate our model. Our model fully leverages contextual information using the Bert model, with a particular emphasis on the interrelationships between sentences. This approach effectively integrates the relationships between sentences and labels, resulting in the creation of an initial data augmentation corpus. Subsequently, filtering algorithms have been devised to enhance the quality of the initial augmentation corpus by eliminating low-quality generated data, ultimately yielding the final text-enhanced dataset. Experimental findings on the Semeval-2014 Laptop and Restaurant datasets demonstrate that the enhanced dataset enhances text quality and markedly boosts the performance of models for aspect-level sentiment classification.

Cross-Domain Aspect-Based Sentiment Classification with a Pre-Training and Fine-Tuning Strategy for Low-Resource Domains

Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting their effectiveness. To overcome this challenge, we propose a low-resource cross-domain aspect-based sentiment classification (CDABSC) approach based on a pre-training and fine-tuning strategy. This approach applies the pre-training and fine-tuning strategy to an advanced deep learning method designed for ABSC, namely the attention-based encoding graph convolutional network (AEGCN) model. Specifically, a high-resource domain is selected as the source domain, and the AEGCN model is pre-trained using a large amount of fine-grained annotated data from the source domain. The optimal parameters of the model are preserved. Subsequently, a low-resource domain is used as the target domain, and the pre-trained model parameters are used as the initial parameters of the target domain model. The target domain is fine-tuned using a small amount of annotated data to adapt the parameters to the target domain model, improving the accuracy of sentiment classification in the low-resource domain. Finally, experimental validation on two domain benchmark datasets, restaurant and laptop, demonstrates significant outperformance of our approach over the baselines in CDABSC Micro-F1.

Harmonizing Perceptions: Exploring Alignment of Numerical and Textual Evaluation Through Fine-Grained Sentiment Analysis

The assessment of faculty teaching effectiveness plays a pivotal role in shaping educational practices and ensuring academic excellence. While numerical evaluations provide quantifiable measures of teaching effectiveness, textual comments offer valuable qualitative insights into the faculty's instructional methods and interactions. However, discrepancies between these evaluation formats may hinder the accurate assessment of faculty performance. This study aims to determine the alignment between the numerical and textual evaluations of faculty teaching effectiveness using fine-grained sentiment analysis and determine the relationship between numerical ratings and textual comments to identify patterns of consistency or divergence. A large dataset of anonymized BSCS students’ feedback is analyzed through sentiment analysis to extract sentiments expressed in the textual comments. The study results reveal a misalignment between students’ subjective perceptions expressed in the textual comments and the numerical ratings. This misalignment arises due to several factors, such as student communication style variations, individual interpretations of rating scales, or subjective biases. This study advances faculty evaluation methodologies and offers useful recommendations for organizations looking to improve assessment procedures as well as educational institutions that want to improve the precision and efficacy of their faculty evaluation systems for better faculty development and decision-making.

How Positive Sentiment of Telemedicine Applications using Naïve Bayes and Support Vector Machine?

The telemedicine software lets users schedule appointments with physicians or hospitals, purchase medications online, read health-related news and publications, and have online consultations with doctors. Public opinion is necessary for this application to meet societal needs. The public's perception of contemporary health applications can be ascertained by looking to public opinion. Fine-grained sentiment analysis is used in public opinion taking to extract information from evaluations posted by the public and categorize it into positive and negative groups. Using RapidMiner as a supporting tool, sentiment analysis uses the Naïve Bayes Classification algorithm and Support Vector Machine to determine an accuracy value. After analyzing data from two different sources—Twitter and Playstore—it can be said that the Support Vector Machine algorithm performs 90.99% more accurately in predicting public opinion than the Naïve Bayes algorithm, which achieves 88.55% accuracy.

ABSA for Chinese review with fine-tuned BERT and zero-shot classification

In recent years, there has been a trend towards utilizing Large Language Models (LLMs) to address diverse issues in computational linguistics. More parameters and layers make them better suited to approximating the objective function in multi-domain tasks. In the twenty-first century, since e-commerce has grown quickly, people's interest in gleaning insightful information from other people's evaluations has grown. Conducting fine-grained sentiment analysis is becoming more beneficial. Aspect-Based Sentiment Analysis (ABSA) is a sub-problem within the field of Sentiment Analysis. The ABSA problem encompasses two sub-problems: Identification of targets and Opinion Mining. Existing methods address ABSA well with a relationship between the aspect and the labels. It is the most common situation in application scenarios. This paper innovatively proposes an ABSA method based on zero-shot classification combined and fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. The method is used experimentally in five tags (1-5) at the "service" and "environment" levels. In the experiment, the predictions of the method-derived model are contrasted with those of the original model. The results show a substantial improvement in the model's prediction accuracy in different aspects.

Twin Towers End to End model for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to conduct fine-grained sentiment analysis, necessitating the extraction of three key components: target entity, aspect category and sentiment polarity. These three components collectively form an integrated ABSA task known as TASD (Target-Aspect-Sentiment jointly Detection). Most of existing approaches on ABSA usually employ Recurrent neural networks(RNNs), Convolutional neural networks(CNNs) or pre-training models such as Bidirectional Encoder Representations from Transformers(BERT). However, they have some common weaknesses. First, most of the existing methods focus on one or two sub-tasks instead of triplet detection, thus they do not establish an end-to-end (training a complex learning system represented by a single model) ABSA model and cannot utilize the relevance of multiple ABSA sub-tasks during training. Second, they cannot achieve accuracy and efficiency simultaneously due to the coupling of context and given aspects. Third, they are poor in recognizing implicit targets. To tackle these limitations, this paper proposes a novel method named the Twin Towers End to End model (TTEE) to solve TASD task. It transforms complex TASD task into a simple end-to-end multi-task framework, simultaneously conducting target and aspect-sentiment detection. It builds twin towers system based on BERT or its updated versions to decouple context and given aspects, which can reduce redundant calculation to improve computational efficiency significantly. It offers great advantage to identify implicit target entity and its associated aspect-sentiment in the context without introducing extra model architecture. Experiments on three public datasets in different domains demonstrate that our approach not only achieves better performance on various evaluation metrics, but also has high efficiency in both training and inference phases, over a wide range of sample size and number of aspect categories.

Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.

Aspect-level multimodal sentiment analysis based on co-attention fusion

AbstractAspect-level multimodal sentiment analysis is the fine-grained sentiment analysis task of predicting the sentiment polarity of given aspects in multimodal data. Most existing multimodal sentiment analysis approaches focus on mining and fusing multimodal global features, overlooking the correlation of more fine-grained multimodal local features, which considerably limits the semantic relevance between different modalities. Therefore, a novel aspect-level multimodal sentiment analysis method based on global–local features fusion with co-attention (GLFFCA) is proposed to comprehensively explore multimodal associations from both global and local perspectives. Specially, an aspect-guided global co-attention module is designed to capture aspect-guided intra-modality global correlations. Meanwhile, a gated local co-attention module is introduced to capture the adaptive association alignment of multimodal local features. Following that, a global–local multimodal feature fusion module is constructed to integrate global–local multimodal features in a hierarchical manner. Extensive experiments on the Twitter-2015 dataset and Twitter-2017 dataset validate the effectiveness of the proposed method, which can achieve better aspect-level multimodal sentiment analysis performance compared with other related methods.

FMSA-SC: A Fine-Grained Multimodal Sentiment Analysis Dataset Based on Stock Comment Videos

FMSA-SC: A Fine-Grained Multimodal Sentiment Analysis Dataset Based on Stock Comment Videos

Lexicon-based prompt for financial dimensional sentiment analysis

Financial sentiment analysis attempts to identify the sentiment of a sentence using dimensional or categorical approaches. The dimensional approach represents affective states as continuous numerical values and can provide more fine-grained (real-valued) sentiment analysis than the categorical approach, which represents affective states as several discrete classes (e.g., positive and negative). Although categorical approaches have been studied extensively over the past decade, the lack of domain-specific data makes it challenging to use dimensional approaches in financial domains. To solve this problem, we propose a lexicon-based prompt method for domains with no labeled data by introducing domain-specific lexicons to correct mispredicted words. As this method does not apply domain-adaptation techniques, it can be combined with domain-adaptation methods to improve the generalizability of domain-specific sentiment analysis further. Experiments on various pre-training language modes in the finance domain show that the proposed lexicon-based prompt method outperforms common domain adaptation methods, and the best performance is achieved by combining multi-task fine-tuning and domain adaptation methods.

Unveiling consumer preferences in automotive reviews through aspect-based opinion generation

Unveiling consumer preferences in online reviews is receiving increasing attention. While most existing approaches for consumer preferences have achieved significant improvements, fine-grained sentiment is rarely considered. Fine-grained sentiment analysis involves several essential tasks, such as aspect-opinion recognition, and sentiment orientation analysis. However, existing methods cannot effectively generate an opinion pair, especially when dealing with Chinese automotive reviews. In this paper, we propose a joint course- and fine-grained sentiment analysis of preferences, a new framework for opinion pair generation using graph neural networks (GCN), which optimizes model performance based on aspect-wise sentiment information, as well as our experiments on the course- and fine-grained tasks. Our graph-based multi-grained convolution (CMGC) model outperforms all baselines by at least 1% accuracy in coarse-grained tasks. The results in the fine-grained task are significantly better than the baseline, surpassing the previous state-of-the-art by 1.33% and 3.88% in R and R@1, respectively. Our results can effectively reveal consumer preferences from automotive reviews, which provides business managers with specific marketing strategies.

Product feature sentiment analysis based on GRU-CAP considering Chinese sarcasm recognition

Sentiment analysis based on product online reviews is an important measure in grasping customer’s satisfaction with products, which has the problem of inadequate feature extraction and difficulty in identifying the sentiment polarity of texts with irony. To solve the problem, this paper proposes a novel method of product feature sentiment analysis based on gate recurrent units and capsule neural network (GRU-CAP). Firstly, product online reviews are preprocessed to build a review data set. The feature extraction model of GRU-CAP is then used to extract the product features contained in the data set and generate the corresponding product feature index. After combining the feature index with the data set to form new input data, the recognition of positive/negative emotions, sarcastic/non-sarcastic semantics is carried out through the sentiment analysis model of GRU-CAP. Finally, the recognition results are integrated to achieve fine-grained product feature sentiment analysis. The effectiveness and practicability of the method is validated by an example product-mobile phone on an e-commerce platform. The results show that the method can extract product features and identify sentiment polarity from product online review texts with irony more accurately, so as to provide better product design support for enhancing customer satisfaction with products more effectively.

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

Span-based dependency-enhanced graph convolutional network for aspect sentiment triplet extraction

Aspect sentiment triplet extraction task detects three elements of fine-grained sentiment analysis from given sentence, including aspect and opinion terms and their sentiment polarity. Existing methods mainly include tagging-based and span-based methods, where the former show defects on handling overlapped triplets, and the latter could theoretically handle all overlapped triplets but lack of tailored inter-word dependency so that suffer from insufficient span semantic. In this paper, we propose a span-based dependency-enhanced graph convolutional network, which leverages contextual semantic and latent dependency to enrich span representations. Specifically, we devise a latent graph convolutional network to emphasize critical inter-word dependencies and cut off redundant connections in a learnable gating manner, improving the information flow during inter-word interaction. In addition, considering the problem of multi-word term sentiment consistency, we detect effective aspect and opinion terms derived from the output of span enumeration, and introduce term-level interactions by coupling, which meanwhile enables our model to deal with various types of triplets including many-to-one and one-to-many overlapped triplets. Extensive experiments over four benchmark datasets verify that the proposed method outperforms all the baselines with an average F1 improvement of up to 6.13%, and meanwhile shows fine interpretability. The experimental results demonstrate that effectively enhancing token-level and term-level interactions can significantly improve the aspect sentiment triplet extraction performance.

Employing synthetic data for addressing the class imbalance in aspect-based sentiment classification

ABSTRACT The class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance.

Tourism forecasting with granular sentiment analysis

Generic sentiment calculations cannot fully reflect tourists' preferences, whereas fine-grained sentiment analysis identifies tourists' precise attitudes. This study forecasted visitor arrivals at two tourist attractions in China using Internet data from multiple sources. Empirical results indicate that 1) fine-grained sentiment analysis of online review data can substantially improve tourism demand models' forecasting performance; 2) combining multidimensional sentiment analysis–based online review data with search engine data outperforms search engine data in tourism demand prediction; and 3) fine-grained sentiment analysis–based online review data and search engine data maintain stable predictive power during times of uncertainty.

Soft prompt enhanced joint learning for cross-domain aspect-based sentiment analysis

Aspect term extraction is a fundamental task in fine-grained sentiment analysis, aiming to detect customer's opinion targets from reviews about products or services. The traditional supervised models have achieved promising results with annotated datasets. However, their performance dramatically decreases in cross-domain aspect term extraction tasks. Existing cross-domain transfer learning methods face two common limitations: (1) these works directly inject linguistic features into language models, making it challenging to transfer linguistic knowledge to the target domain; (2) they rely on the fixed predefined prompts, which is time-consuming to construct the prompts for all potential aspect term spans. To address the limitations, we propose a soft prompt-based joint learning method for cross-domain aspect term extraction in this paper. Specifically, by incorporating external linguistic features, the proposed method learns domain-invariant representations between source and target domains via multiple objectives, which bridges the gap between domains with varied distributions of aspect terms. Furthermore, the proposed method interpolates a set of transferable soft prompts consisting of multiple learnable vectors that are beneficial to detect aspect terms in the target domain. Extensive experiments are conducted on two groups of datasets and the experimental results demonstrate the effectiveness of the proposed method for cross-domain aspect terms extraction.

Emotion quantification and classification using the neutrosophic approach to deep learning

Advancements in the rapidly evolving specialization of deep learning have aided in improving several natural language understanding tasks. Sentiment and emotion classification models have improved, but when it comes to fine-grained sentiment analysis, these models can perform better. Human sentiment in natural language is generally an intricate combination of emotions, which can sometimes be indeterminate, neutral, or ambiguous. In the case of fine-grained sentiment analysis, the sentiments can be very similar to each other and interconnected, e.g., anger and fear. Most deep learning systems try to solve the problem of fine-grained sentiment analysis as a classification problem. However, fine-grained sentiments might combine similar emotions with one primary emotion. Trying to solve the problem as a classification task can result in better performance on benchmarks but does not ensure a better understanding and representation of language. The proposed work explores applying neutrosophy for fine-grained sentiment analysis using large language models. Neutrosophy identifies neutralities and employs membership functions (neutral, positive, negative) to quantify an instance into Single Valued Neutrosophic Sets (SVNS). This paper introduces Refined Emotion Neutrosophic Sets (RENS) for emotions (with four emotions) and Refined Ekman’s Emotion Neutrosophic Sets (REENS) with seven emotions. In this paper, refined neutrosophic sets with membership functions are employed for each sentiment across a given taxonomy and assigned their values using the Neutrosophic Iterative Neural Clustering (NINC) algorithm proposed in this paper. It facilitates not only classifying sentiments but also quantifying the presence of each sentiment present in a given sample. It aids in better understanding and representation of samples across multiple sentiments, as in fine-grained sentiment analysis, experiments are performed on the GoEmotions dataset. The proposed approach performs on par with cross-entropy deep learning classifiers and is reproducible across different pre-trained language models.

Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this paper, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.