Quality achhi hai (is good), satisfied! Towards aspect based sentiment analysis in code-mixed language

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step solution to predict both aspect terms and their sentiment polarities through a single decoding process, which avoid the mismatches in between the results of aspect terms and sentiment polarities, as well as error propagation. Previous studies, especially recent ones, for this task focus on using powerful encoders (e.g., Bi-LSTM and BERT) to model contextual information from the input, with limited efforts paid to using advanced neural architectures (such as attentions and graph convolutional networks) or leveraging extra knowledge (such as syntactic information). To extend such efforts, in this paper, we propose directional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated in our model to enhance its ability of representing input sentences and help EASA accordingly. Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, where D-GCN achieves state-of-the-art performance on all datasets.

Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i.e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency. To overcome the problems mentioned above, in this work we present a novel one-stop solution on aspect-based sentiment analysis. Specifically, we propose a novel multi-task neural learning framework to jointly tackle aspect extraction and sentiment prediction subtasks at the same time, and leverage attention mechanisms to learn the joint representation of aspect-sentiment relationship. We have conducted extensive comparative experiments on two benchmark datasets from SemEva1–2014. The experiment results demonstrate the effectiveness of our proposed solution. Especially, our multi-task model outperforms the state-of-the-art systems on aspect extraction subtask.

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Aspect-Based Sentiment Analysis (ABSA) is essential for extracting detailed sentiment polarity regarding specific aspects in product reviews, providing deeper insights into customer opinions on various product attributes. Unlike document-level sentiment analysis, ABSA allows a more granular understanding, crucial for e-commerce analytics and decision-making systems. This study investigates the effectiveness of transformer-based models, such as BERT and RoBERTa, in performing ABSA for product review mining.Purpose:This research aims to explore the application of transformer-based models for aspect-based sentiment analysis, comparing their performance with traditional deep learning models (LSTM and CNN) in the context of mining product reviews. The study evaluates how transformer-based models can more effectively capture sentiment polarity at the aspect level.Methods/Study design/approach:The study uses publicly available product review datasets from large-scale e-commerce platforms, where each review is annotated with aspect terms and sentiment polarities (positive, negative, neutral). The datasets were split into training, validation, and test sets in an 80:10:10 ratio. The models—BERT, RoBERTa, LSTM, and CNN—were fine-tuned on the ABSA task. Performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.Result/Findings:The results show that transformer-based models, especially RoBERTa, significantly outperform conventional deep learning baselines like LSTM and CNN. RoBERTa achieved the best performance with an accuracy of 0.93 and an F1-score of 0.92, while BERT achieved an accuracy of 0.91 and an F1-score of 0.90. In contrast, LSTM and CNN achieved F1-scores of 0.82 and 0.84, respectively. The transformer models excel in capturing contextual dependencies and associating sentiment polarity with the correct aspects, particularly in complex and multi-aspect sentences. This highlights the superior ability of transformers to handle long-range dependencies and complex sentence structures compared to LSTM and CNN.The findings confirm that transformer-based models are highly effective for aspect-based sentiment analysis, providing a more reliable approach for product review mining. Future research should address the efficiency and interpretability of these models, particularly for large-scale deployment in real-world e-commerce applications.

Aspect-based sentiment analysis (ABSA) aims to detect the sentiment polarities of a sentence with a given aspect. Aspect-term sentiment analysis (ATSA) is a subtask of ABSA, in ATSA, the aspect is given by aspect term: a word or a phrase in sentence. In previous work, most models apply attention mechanism or gating mechanism to capture the key part of the sentence and detect the sentiment polarity by classifying the weighted sum vector, which are regardless of the aspect information during the classification. However, same contexts may show different sentiment polarity with differnet aspects. For example, in sentence “The scene hunky waiters dub diner darling and it sounds like they mean it.” the context “dub diner dearling” shows negative polarity towards the aspect term “waiters”. But in sentence “The diner's husband dub diner darling.”, the same context would show neural polarity towards “husband”. The absent of aspect information would make model get a wrong result in some sentence. To solve this problem, we propose a model called Trasfomer-based context-aspect Relation Detect Model (TRDM), and add two special tokens “[CLSA]” and “[SEPA]” to specify the aspect term in the sentence. TRDM uses the embedding of two tyes of tokens (i.e. “[CLS]” and “[CLSA]”) for classification where “[CLS]” is used to represent the entire sentence in BERT. This scheme enable TRDM to combine the sentence information and aspect information in the step of sentiment polarity classification. We evaluate the performance of our model on two datasets: Restaurant dataset from SemEval2014 and MAMS from NLPCC 2020. Experiment results show that our model obtain noticeable improvement compared with state-of-art transfromer-based models.

Sentiment analysis is becoming rapidly important for exploring social media Bangla text. The lack of sufficient resources is considered to be an important challenge for Aspect Based Sentiment Analysis (ABSA) of the Bangla language. The ABSA is a technique that divides the text and defines its sentiment based on its aspects. In this paper, we developed a high-quality Bangla ABSA annotated dataset namely BANGLA_ABSA. The datasets are labelled with aspects category and their respective sentiment polarity to do the ABSA task in Bangla. Four open domains namely Restaurant, Movie, Mobile phone, and Car are considered to make the dataset. The datasets are called Restaurant_ABSA, Movie_ABSA, Mobile_phone_ABSA, and Car_ABSA respectively that contain 801, 800, 975, and 1149 comments. All the comments are either complex or compound sentences. We created the dataset manually and annotated the same by exerting opinions. We organized the dataset as three tuples in Excel format namely 〈Id, Comment, {Aspect category, Sentiment Polarity}〉. These data are very important that facilitate the efficient handling of sentiment for any machine learning and deep learning research, especially for Bangla text.

Sentiment analysis (SA) is one of the most active and rapidly developing research areas in both information retrieval and text mining fields of computer science. SA is the process of analysing, handling, generalizing, and reasoning about user sentiments, opinions, and emotions hidden within the text by using sentiment. There are various categories of levels of SA: (1) document-level, (2) sentence-level, and (3) aspect-level. This paper is focused on sorting out some Aspect-based sentiment analysis (ABSA) works. ABSA is one of the primary tasks in sentiment analysis, which involves identifying the expression of an opinion or sensation of an object in a particular dimension or feature. ABSA involves two primary subtasks: (1) aspect extraction and (2) aspect-based sentiment classification. In contrast to document-and sentence-based sentiment analysis, ABSA simultaneously considers sentiment and target information. Generally, the ABSA methods can be categorized into Transformer based ABSA and Graph Neural Network based ABSA. This paper introduces some popular frameworks, datasets, and evaluation metrics of ABSA tasks based on the above-proposed classification and methodology.

Aspect-based sentiment analysis includes opinion aspect extraction and aspect sentiment classification. Researchers have attempted to discover the relationship between these two sub-tasks and have proposed the joint model for solving aspect-based sentiment analysis. However, they ignore a phenomenon: aspect boundary label and sentiment label of the same word can correct each other. To exploit this phenomenon, we propose a novel deep learning model named the label correction model. Specifically, given an input sentence, our model first predicts the aspect boundary label sequence and sentiment label sequence, then re-predicts the aspect boundary (sentiment) label sequence using the embeddings of the previously predicted sentiment (aspect boundary) label. The goal of the re-prediction operation (can be repeated multiple times) is to use the information of the sentiment (aspect boundary) label to correct the wrong aspect boundary (sentiment) label. Moreover, we explore two ways of using label embeddings: add and gate mechanism. We evaluate our model on three benchmark datasets. Experimental results verify that our model achieves state-of-the-art performance compared with several baselines.

Sentiment analysis or opinion mining used to capture the community's attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.

In the expanding field of sentiment analysis, the integration of personality prediction into aspect-based sentiment analysis (ABSA) represents a novel and promising approach to enhance the accuracy and depth of sentiment detection. This paper proposes a unique framework that leverages the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) alongside Long Short-Term Memory (LSTM) networks, under a multitask learning paradigm, to improve the performance of ABSA. This is, to the best of knowledge, the first work considering the use of personality traits as auxiliary tasks in order to capture the manifold subtle ways in which personality would influence the expression of sentiment towards the different aspects of products or services. And then, model uses the LSTM component to model the sequential character of the text, which makes the extraction accurate in terms of the aspect terms and sentiment polarities. The proposed model designs a multitask learning strategy simultaneously to predict sentiments and personality traits. Such joint learning will allow enhancing the model's understanding of textual context and sentiment expression. Thorough experiments on many benchmark datasets show that the proposed approach is competitive with the state of the art for the aspect-based sentiment analysis and provides some of the deepest insights into personality predictions. Model has obtained F1-scores of 79.78%, 83.67%, and 88.80 % on the Twitter, Laptop, and Restaurant datasets, respectively. These results highlight a significant improvement over existing methods in the literature. For instance, our model outperformed traditional approaches like RAM, which achieved 69.36% on the Twitter dataset, and even advanced techniques such as DualGCN+Bert, which scored 77.4% on Twitter. It can be generally concluded that this research finally opens the way to a new and meaningful opportunity for sentiment analysis applications: integrated into ABSA models, personality prediction advances applications ranging from personalized recommendation systems to the nuance market analysis tools. As far as we know this study is the first attempt to utilise personality feature to enhance sentiment prediction tasks.

Aspect-Based Sentiment Analysis (ABSA) studies the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize better can be laborious and time-consuming. As an alternative, similar data to the real-world examples can be produced artificially through an adversarial process which is carried out in the embedding space. Although these examples are not real sentences, they have been shown to act as a regularization method which can make neural networks more robust. In this work, we fine- tune the general purpose BERT and domain specific post-trained BERT (BERT-PT) using adversarial training. After improving the results of post-trained BERT with different hyperparameters, we propose a novel architecture called BERT Adversarial Training (BAT) to utilize adversarial training for the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis. The proposed model outperforms the general BERT as well as the in-domain post-trained BERT in both tasks. To the best of our knowledge, this is the first study on the application of adversarial training in ABSA. The code is publicly available on a GitHub repository at https://github.com/IMPLabUniPr/Adversarial-Training-for-ABSA

Reading massive amounts of user-generated text and pulling out the relevant aspects and opinions is a complicated process. Summaries, on the other hand, help busy people who only have a little bit of time to read get the gist of the information quickly. Text summarization is the process of taking the original text and making a shorter version of it that still has all of its informational value and main idea. Humans have a hard time summarizing long texts by hand. Different ways to summarize a text can be put into groups based on the more general techniques of extractive and abstractive summarization. The research paper discusses the need for generating aspect-based summaries and Sentiment analysis. A framework is proposed based on extracting coherent aspects from the reviews and applying the extractive summarization method to generate summaries. In addition, providing insights into the reviews of tourist attractions by using aspect-based sentiment analysis. The results are evaluated using crowdsourcing, Fairsumm, and Centroid method. The crowdsourcing method gives the best result on aspect-based summaries.

Social media has become an essential part of everyday communication, including within the library context. Twitter, in particular, provides an Application Programming Interface (API) that enables real-time and comprehensive text-mining analyses of users’ perceptions and experiences. This study examines efforts to enhance library service quality by identifying and interpreting user sentiments on Twitter in a more detailed manner, enabling libraries to formulate targeted and effective service improvement strategies. The research consists of several stages: a literature review on sentiment analysis, data collection from Twitter using API-based data crawling, and a series of pre-processing steps, including data cleaning, case folding, tokenization, numerization, stopword removal, and stemming. Data were analyzed using the Aspect-Based Sentiment Analysis (ABSA) method. The findings indicate that the Collection and Facilities aspects receive the highest levels of positive sentiment, while Accessibility and Service attract considerable negative sentiment, signaling priority areas for improvement. Based on these results, libraries may consider extending operational hours, improving digital access, updating collections, and renovating facilities. Additionally, strengthening staff competencies and interpersonal skills is crucial for improving service quality and increasing user satisfaction.

With proliferation of user-generated reviews, new opportunities and challenges arise. The advance of Web technologies allows people to access a large amount of reviews of products and services online. Knowing what others like and dislike becomes increasingly important for their decision making in online shopping. The retailers also care more than ever about online reviews, because a vast pool of reviews enables them to monitor reputations and collect feedbacks efficiently. However, people often find difficult times in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional sentiment analysis, which focuses on the overall sentiments, fails to uncover the sentiments with regard to the aspects of the reviewed entities. This dissertation studied the research problem of Aspect Based Sentiment Analysis (ABSA), which is to reveal the aspect-dependent sentiment information of review text. ABSA consists of several subtasks: 1) aspect extraction, 2) aspect term extraction, 3) aspect category classification, and 4) sentiment polarity classification at aspect level. We focused on the approach of topic models and neural networks for ABSA. First, to extract the aspects from a collection of reviews and to detect the sentiment polarity regarding the aspects in each review, we proposed a few probabilistic graphical models, which can model words distribution in reviews and aspect ratings at the same time. Second, we presented a multi-task learning model based on long-short term memory and convolutional neural network for aspect category classification and aspect term extraction. Third, for aspect-level sentiment polarity classification, we developed a gated convolution neural network, which can be applied to aspect category sentiment analysis as well as aspect target sentiment analysis.

The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.