Semantics perception and refinement network for aspect-based sentiment analysis

Aspect based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, whose main goal is to identify the sentiment polarity of an aspect in a sentence. A sentence may contain many different aspects, each of which may have different sentiment polarities. Based on the current researches in this area, ABSA can be divided into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). In the past, more commonly used method is to adopt the time serial algorithm such as Long Short-Term Memory (LSTM) or Recurrent Neural Network (RNN), which usually needs more training time and has complex structures. Moreover, many previous models lack the abilities to effectively learn the internal structure features of sentences. For ABSA, sometimes the sentence structure may significantly affect the final classification results. However, we found the excellent performance of self-attention algorithm and gating mechanism in some other related researches. Therefore, to solve the problems above, we build a new model based on gating mechanism, combined with convolutional neural networks (CNN) and self-attention mechanism. First, we use self-attention to extract the structural feature of the input, and integrate it with the features of the original sentence extracted by CNN. On such basis, we further combine the aspect-category or aspect-term of the input sentence to form the final sentiment feature. Experiments on SemEval datasets show the performance of our models and the effectiveness of the model is proved.

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.

With the extensive growth of user interactions through prominent advances of the Web, sentiment analysis has obtained more focus from an academic and a commercial point of view. Recently, sentiment analysis in the Bangla language is progressively being considered as an important task, for which previous approaches have attempted to detect the overall polarity of a Bangla document. To the best of our knowledge, there is no research on the aspect-based sentiment analysis (ABSA) of Bangla text. This can be described as being due to the lack of available datasets for ABSA. In this paper, we provide two publicly available datasets to perform the ABSA task in Bangla. One of the datasets consists of human-annotated user comments on cricket, and the other dataset consists of customer reviews of restaurants. We also describe a baseline approach for the subtask of aspect category extraction to evaluate our datasets.

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.

Aspect Based Sentiment Analysis (ABSA) is a sentiment analysis technique that not only identifies overall sentiment, but also reveals opinions on specific aspects of an entity. To facilitate computer processing, a numerical representation of words into vectors (word embedding) is used, where each word or phrase is mapped into a vector of dimension N. Although static embedding such as Word2Vec or GloVe has been widely used, these approaches have limitations in capturing the dynamic context essential for deep sentiment analysis. This research develops and tests several deep learning algorithms, namely CNN, Bi-LSTM, CNN+BiLSTM, and CNN+BiLSTM+Attention Mechanism, which initially use static embedding and then modified by integrating BERT as contextual embedding. The results show that the use of BERT improves sentiment prediction accuracy by 15% and aspect prediction accuracy by 11% compared to models with static embedding. In particular, the combination of BERT+CNN obtained the best accuracy, which was 94% for aspect prediction and the combination of BERT+CNN+BiLSTM+Attention Mechanism 87% for sentiment prediction. These findings demonstrate the significant potential of BERT integration in improving ABSA performance, which can be applied in social media opinion analysis and sentiment-based recommendation systems.

Aspect-based sentiment analysis as fine-grained sentiment analysis problem, mainly aims to clarify the sentiment of different aspect. Previous fine-grained sentiment analysis methods usually used Recurrent Neural Networks as feature extractors, and achieved good results in sentiment analysis. However, they were limited to the sequential processing of sequence by RNN. Its training and operation efficiency are relatively low and time consuming. Aiming at this problem, combining the multi-head attention mechanism in Transformer, fusion model of convolution neural network and hierarchical attention coding network is proposed, and convolution neural network is used as feature encoder to improve the efficiency of model training. At the same time, multi-head attention is used to calculate the degree of relevance between aspect words and context, thus capturing emotional elements corresponding to entity words. Experiments on the SemEval2014 dataset have improved the training and inference efficiency of aspect-based sentiment analysis.

Consumer opinions on purchased products significantly influence the number of purchases on ecommerce platforms. Sentiment analysis of these opinions assists both consumers and companies in making informed decisions. Currently, consumers judiciously evaluate different aspects of a product to guarantee it meets their requirements. Consequently, performing sentiment analysis on various product aspects can aid consumers select the precise product and support companies in meeting on areas that prerequisite upgrading. This study recommends a systematic and expandable method to aspect-based sentiment analysis of user reviews for electrical devices like computers, mobile phones, and headphones. Through an amalgamation of advanced preprocessing, sentence-level segmentation by means of transformer-based models, and similarity-based aspect detection, the system is competent of extracting fine-grained sentiment for each product aspect. A hybrid sentiment classification strategy employing both BERT and VADER certifies robustness and accurateness in sentiment detection, while the structured transformation of data into a multi-label format permits for efficient model training. The proposed Bi-LSTM architecture with an integrated attention mechanism boosts sentiment prediction by concentrating on the utmost pertinent parts of each sentence, directing to better-quality performance across evaluation metrics, including accuracy, precision, recall, and F1-score. The exploration offers a comparative study of the performance of aspect-based sentiment analysis using a standard Bi-LSTM model versus a Bi-LSTM model with an attention mechanism, applied to analyse the reviews of three diverse products. The outcomes explains that the Bidirectional Long Short-Term Memory (Bi-LSTM) model with an attention mechanism outpaces the Bidirectional Long Short-Term Memory (Bi-LSTM) in terms of accuracy, precision, recall, and F1-score. The investigation validates the efficiency of merging NLP techniques and deep learning to deliver understandings from product reviews, presenting beneficial assistance to consumers by aiding to make informed decisions and focus on companies directing to improve their offerings based on targeted feedback.

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.

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.

Sentiment analysis is a type of opinion mining study analyzing people's thoughts, feelings, and assessments evaluations of society things such as products, services, and institutions individuals, organizations, events and etc. This paper focuses on an effective machine learning model, Recurrent Neural Network (RNN), for sentiment analysis of both Bengali and English text. For sentiment analysis of Bengali text, we construct a novel dataset accumulated from different open source sites and social media. The dataset comprises over 17,000 texts, which are labelled as neutral, positive, and negative. Our study provides a rigorous comparative analysis on the performance of RNN in terms of sentiment analysis of both raw text and corresponding translated text (for both English and Bengali language). In summary, we achieve 82.9% and 84.5% accuracies for sentiment analysis of Bengali and English text respectively using RNN.

ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis

The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.

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) aims to analyze the sentiment polarity of an input sentence in a certain aspect. Many existing methods of ABSA employ long short-term memory (LSTM) networks and attention mechanism. However, the attention mechanism only models the local certain dependencies of the input information, which fails to capture the global dependence of the inputs. Simply improving the attention mechanism fails to solve the issue of target-sensitive sentiment expression, which has been proven to degrade the prediction effectiveness. In this work, we propose the multi-head self-attention transformation (MSAT) networks for ABSA tasks, which conducts more effective sentiment analysis with target specific self-attention and dynamic target representation. Given a set of review sentences, MSAT applies multi-head target specific self-attention to better capture the global dependence and introduces target-sensitive transformation to effectively tackle the problem of target-sensitive sentiment at first. Second, the part-of-speech (POS) features are integrated into MSAT to capture the grammatical features of sentences. A series of experiments carried on the SemEval 2014 and Twitter datasets show that the proposed model achieves better effectiveness compared with several state-of-the-art methods.