Aspect Detection Enhancement for Aspect Based Sentiment Analysis

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.

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.

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)

In the ever-expanding digital landscape, the abundance of user-generated content on consumer platforms such as Booking and TripAdvisor offers a rich source of information for both travellers and hoteliers. Sentiment analysis, a fundamental research task of natural language processing (NLP) is used for mining sentiments and opinions within this vast reservoir of text reviews. A more specific type of sentiment analysis, i.e., aspect-based sentiment analysis (ABSA), is used when processing customer reviews is required. In ABSA, we aim to capture aspect-level sentiments and intricate relationships between various aspects within reviews. This article proposes a novel approach to ABSA by introducing a novel technique of word sense disambiguation (WSD) and integrating it with the Transformer architecture bidirectional encoder representations from Transformers (BERT) and graph convolutional networks (GCNs). The proposed approach resolves the intriguing ambiguities of the words and represents the review data as a complex graph structure, facilitating the modeling of intricate relationships between different aspects. The combination of bidirectional long short-term memory (BiLSTM) and GCN proves effective in capturing inter-dependencies among various aspects, providing a nuanced understanding of customer sentiments. The experiments are conducted on the RABSA dataset (an enhanced and richer hotel review data collection), and results demonstrate that our approach outperforms previous baselines, showcasing the effectiveness of integrating WSD in ABSA. Furthermore, an ablation study confirms the significant contribution of the WSD module to the overall performance. Moreover, we explore different similarity measures and find that cosine similarity yields the best results when identifying the real sense of a word in a given sentence using WordNet. The findings of our work and future work related to our work create lots of interest for people in the tourism and hospitality industry. This research gives another boost to the concept of the potential of NLP techniques in sentiment analysis. It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.

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.

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.

Social networking sites are now entered into the mainstream media landscape and cover a broad demographic spectrum of Internet users. Users post real time messages about their opinions on different topics and current issues, complain and express sentiments for products they use in their daily life. Such users have produced extensive online user-generated content and attracted business firms to consider it as a valuable input for their business processes. But this rapid growth in data size has posted many challenges in accessing, managing, analyzing, and integrating the data in networking, management, computational, and statistical sciences. This paper proposes a framework for analyzing the online data produced by the social networking site, twitter. The proposed Jen-Ton framework collects, pre-processes and analyses the sentiment present in the tweets and prepares the aspect based sentiment analysis for a product. This framework uses three techniques namely, Imputation of Missing Sentiment (IMS), Aspect based Sentiment Analysis using Fuzzy Logic (ASFuL) and Aspect based SenTiment Analysis using Clustering with Genetic Algorithm (ASTA-CGA) for enhancing the accuracy of the aspect based sentiment analysis in big data.

The rapidly evolving landscape of Aspect-Based Sentiment Analysis (ABSA) in the realm of natural language processing necessitates innovative approaches to comprehend and interpret the intricate nature of sentiments expressed in textual data. Traditional ABSA methods have often struggled with the nuanced sentiments inherent in various textual sources, limited in their ability to adapt to domain-specific vernacular and context. This study introduces a novel approach that synergizes rule mining with advanced deep learning techniques, aiming to address these limitations and enhance the precision and contextual understanding in sentiment analysis. Our proposed model integrates rule-based systems with deep learning Transformers, a method recognized for its effectiveness in extracting structured, domain-specific insights. This integration results in a significant enhancement in the model's ability to capture nuanced sentiments, as demonstrated by an 8.5% increase in aspect-based sentiment analysis precision and an 8.3% improvement in accuracy over existing methods. The model employs a combination of techniques including Bi-LSTM, BiGRU-CRF with RNNs, and ontology mapping operations, forming a Hybrid Multi-Level Architecture that effectively captures both sequential data relationships and semantic context. Additionally, the model incorporates Cross-Domain Transfer Learning, utilizing BERT-based pre-trained models with added layers for contextual semantics, which has shown notable generalization capabilities across various domains. Furthermore, this study refines evaluation parameters, tailoring metrics such as domain-specific accuracy, recall, and precision to more accurately assess model performance in particular domains. This is especially pertinent in our analysis of Twitter datasets specific to industries like finance and healthcare. The inclusion of Temporal Dynamics and Aspect Summarization, using techniques like the VARMAx process, provides insights into the evolution of sentiments and aspects over time, an aspect crucial for longitudinal sentiment analysis. The comprehensive testing of our model on diverse spatial and temporal datasets reveals not only improved precision and accuracy in sentiment analysis but also a reduction in delay, demonstrating its efficiency and responsiveness. The study's findings indicate that our approach could significantly impact fields reliant on sentiment analysis, such as market analysis, public opinion research, and social media monitoring, providing a more nuanced and accurate understanding of consumer sentiments and trends. This paper's contribution lies in its innovative amalgamation of rule-based and deep learning techniques, tailored evaluation metrics, and its emphasis on temporal dynamics, setting a new precedent in the field of ABSA and opening avenues for further research and application in real-world scenarios.

Public opinion is important for decision-making on numerous occasions for national growth in democratic countries like Bangladesh, the USA, and India. Sentiment analysis is a technique used to determine the polarity of opinions expressed in a text. The more complex stage of sentiment analysis is known as Aspect-Based Sentiment Analysis (ABSA), where it is possible to ascertain both the actual topics being discussed by the speakers as well as the polarity of each opinion. Nowadays, people leave comments on a variety of websites, including social networking sites, online news sources, and even YouTube video comment sections, on a wide range of topics. ABSA can play a significant role in utilizing these comments for a variety of objectives, including academic, commercial, and socioeconomic development. In English and many other popular European languages, there are many datasets for ABSA, but the Bengali language has very few of them. As a result, ABSA research on Bengali is relatively rare. In this paper, we present a Bengali dataset that has been manually annotated with five aspects and their corresponding sentiment. A baseline evaluation was also carried out using the Bidirectional Encoder Representations from Transformers (BERT) model, with 97% aspect detection accuracy and 77% sentiment classification accuracy. For aspect detection, the F1-score was 0.97 and for sentiment classification, it was 0.77.

The plethora of online reviews available on the internet has great insights about various products or entities. However, selecting or recommending a product by sifting through these reviews is a tedious and time consuming task. Sentiment analysis of such reviews helps understand the pros or cons of the product. In recent years researchers and industry people have leveraged such reviews to rank or recommend the products to new users. Ranking or recommending products using text reviews is a challenging task. In this study, we propose a framework to get a ranking of products using aspect-based sentiment analysis (ABSA). ABSA is a fine-grained sentiment analysis task that is useful for extracting opinions about the different aspects of a product. To facilitate automated ABSA, a novel approach for aspect sentiment classification is proposed in this study. Further to rank the products based on aspect-specific performance, a framework is developed using Multi-criteria decision-making (MCDM) techniques. A case study is performed in the automobile domain to rank various cars using the ABSA approach. To validate the effectiveness of the proposed approach, the ranking results are compared with the numeric ratings available for various aspects of the car. The experimental results show that the ranking obtained by the proposed approach matches the numerical ratings. Therefore, the car selection problem or recommendation of cars in the automobile domain can be effectively handled using the proposed framework.

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.