Sentiment analysis is crucial for brand reputation management in the banking sector, where customer feedback spans English, Sinhala, Singlish, and code-mixed text. Existing models struggle with low-resource languages like Sinhala and lack interpretability for practical use. This research develops a hybrid aspect-based sentiment analysis framework that enhances multilingual capabilities with explainable outputs. Using cleaned banking customer reviews, we fine-tune XLM-RoBERTa for Sinhala and code-mixed text, integrate domain-specific lexicon correction, and employ BERT-base-uncased for English. The system classifies sentiment (positive, neutral, negative) with confidence scores, while SHAP and LIME improve interpretability by providing real-time sentiment explanations. Experimental results show that our approaches outperform traditional transformer-based classifiers, achieving 92.3 percent accuracy and an F1-score of 0.89 in English and 88.4 percent in Sinhala and code-mixed content. An explainability analysis reveals key sentiment drivers, improving trust and transparency. A user-friendly interface delivers aspect-wise sentiment insights, ensuring accessibility for businesses. This research contributes to robust, transparent sentiment analysis for financial applications by bridging gaps in multilingual, low-resource NLP and explainability.
University of Moratuwa（莫拉图瓦大学）；LIRNEasia（LIRNEasia（亚洲区域信息学研究组织））
The Facebook network allows its users to record their reactions to text via a typology of emotions. This network, taken at scale, is therefore a prime data set of annotated sentiment data. This paper uses millions of such reactions, derived from a decade worth of Facebook post data centred around a Sri Lankan context, to model an eye of the beholder approach to sentiment detection for online Sinhala textual content. Three different sentiment analysis models are built, taking into account a limited subset of reactions, all reactions, and another that derives a positive/negative star rating valu…
The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To achieve this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model …
Due to the high impact of the fast-evolving fields of machine learning and deep learning, Natural Language Processing (NLP) tasks have further obtained comprehensive performances for highly resourced languages such as English and Chinese. However Sinhala, which is an under-resourced language with a rich morphology, has not experienced these advancements. For sentiment analysis, there exists only two previous research with deep learning approaches, which focused only on document-level sentiment analysis for the binary case. They experimented with only three types of deep learning models. In con…
SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
Dept. of Computer Science & Engineering, University of Moratuwa, Sri Lanka（印度 Moratuwa 大学计算机科学与工程系，斯里兰卡）；School of Technologies, Cardiff Metropolitan university, United Kingdom（英国卡迪夫米大学科技学院）
Sentiment analysis has been widely used by businesses for social media opinion mining, especially in the financial services industry, where customers' feedbacks are critical for companies. Recent progress of neural network models has achieved remarkable performance on sentiment classification, while the lack of classification interpretation may raise the trustworthy and many other issues in practice. In this work, we study the problem of improving the explainability of existing sentiment classifiers. We propose two data augmentation methods that create additional training examples to help impr…
The increasing use of social media sites in countries like India has given rise to large volumes of code-mixed data. Sentiment analysis of this data can provide integral insights into people's perspectives and opinions. Developing robust explainability techniques which explain why models make their predictions becomes essential. In this paper, we propose an adequate methodology to integrate explainable approaches into code-mixed sentiment analysis.
FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis
Asian Institute of Digital Finance（亚洲数字金融学院）；National University of Singapore（新加坡国立大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Nanyang Technological University（南洋理工大学）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；College of Design & Engineering（设计与工程学院）
Current Explainable AI (ExAI) methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a common evaluation framework is hindering the progress tracking of such methods and their wider adoption. In this work, inspired by offline information retrieval, we propose different metrics and techniques to evaluate the explainability of SA models from two angles. First, we evaluate the strength of the extracted "rationales" in faithfully explaining the predicted outcome. Second, we measure the agreement between ExAI m…
Progress on many Natural Language Processing (NLP) tasks, such as text classification, is driven by objective, reproducible and scalable evaluation via publicly available benchmarks. However, these are not always representative of real-world scenarios where text classifiers are employed, such as sentiment analysis or misinformation detection. In this position paper, we put forward two points that aim to alleviate this problem. First, we propose to extend text classification benchmarks to evaluate the explainability of text classifiers. We review challenges associated with objectively evaluatin…
A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets
Khulna University of Engineering & Technology (KUET)（库尔纳工程技术大学）；Advanced Machine Intelligence Research (AMIR) Lab（高级机器智能研究实验室）；American International University-Bangladesh（孟加拉国美国国际大学）；Chowagiken Corp.（Chowagiken公司）；University of Asia Pacific（亚太大学）；University of Aizu（会津大学）
University of Moratuwa（莫拉图瓦大学）；LIRNEasia（斯里兰卡信息通信技术促进发展区域倡议组织）
In the process of numerically modeling natural languages, developing language embeddings is a vital step. However, it is challenging to develop functional embeddings for resource-poor languages such as Sinhala, for which sufficiently large corpora, effective language parsers, and any other required resources are difficult to find. In such conditions, the exploitation of existing models to come up with an efficacious embedding methodology to numerically represent text could be quite fruitful. This paper explores the effectivity of several one-tiered and two-tiered embedding architectures in rep…
Arabic Sentiment Analysis with Noisy Deep Explainable Model
Virginia Tech（弗吉尼亚理工大学）；Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所）；University of Siegen（锡根大学）；UITS（达卡大学信息技术学院）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡根应用科学大学）
Aston University（阿斯顿大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Birmingham City University（伯明翰城市大学）；University of Münster（明斯特大学）；George Mason University（乔治梅森大学）
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (…
Hong Kong University of Science and Technology（香港科技大学）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Management University（新加坡管理大学）
Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite…
Evaluating Compositional Approaches for Focus and Sentiment Analysis
Universidade da Coruña, Grupo LyS, CITIC, Depto. de Ciencias de la Computación y Tecnologías de la Información, Campus de Elviña s/n, 15071 A Coruña, Spain, School of International Letters \& Cultures, Arizona State University, Tempe, Arizona, United States Beus CXFEL Laboratory, Biodesign Institue \& Physics Department, College of Liberal Art \& Sciences, Arizona State University, Tempe, Arizona, United States University of Augsburg, The Applied Computational Linguistics (ACoLi) Lab, Universitätsstr. 10, 86159 Augsburg, Germany
Deep neural networks are increasingly used in natural language processing (NLP) models. However, the need to interpret and explain the results from complex algorithms are limiting their widespread adoption in regulated industries such as banking. There has been recent work on interpretability of machine learning algorithms with structured data. But there are only limited techniques for NLP applications where the problem is more challenging due to the size of the vocabulary, high-dimensional nature, and the need to consider textual coherence and language structure. This paper develops a methodo…
Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)
Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USA（波士顿大学计算机科学系）；Faculty of Computer Science（计算机科学系）；Methodius University, 1000 Skopje, North Macedonia（Methodius大学）
Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model
Harbin Institute of Technology（哈尔滨工程大学）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；University of Science and Technology of China（中国科学技术大学）
Understanding Public Perception of Crime in Bangladesh: A Transformer-Based Approach with Explainability
Chittagong University of Engineering and Technology（吉大港工程技术大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；European University of Bangladesh（孟加拉国欧洲大学）
Application of integrated gradients explainability to sociopsychological semantic markers
Department of Information Engineering, University of Padova（帕多瓦大学信息工程系）；Center for Research on Social Relations, University of Social Sciences and Humanities (SWPS)（社会科学与人文大学社会关系研究中心）；Department of Cognitive Science, Nicolaus Copernicus University in Toruń（托伦尼古拉·哥白尼大学认知科学系）；Interdisciplinary Centre for Modern Technologies, Nicolaus Copernicus University in Toruń（托伦尼古拉·哥白尼大学现代技术跨学科中心）；Department of Developmental Psychology and Socialization, University of Padova（帕多瓦大学发展心理学与社会化系）
Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI
Department of Computer Science（计算机科学系）；University of South Carolina（南卡罗来纳大学）；Department of Civil, Environmental, and Ocean Engineering（土木、环境与海洋工程系）；Stevens Institute of Technology（史蒂文斯理工学院）；School of Computing（计算学院）；University of Nebraska–Lincoln（内布拉斯加大学林肯分校）；Bowling Green State University（鲍灵格林州立大学）
