Sentiment analysis has been widely used to understand our views on social and political agendas or user experiences over a product. It is one of the cores and well-researched areas in NLP. However, for low-resource languages, like Bangla, one of the prominent challenge is the lack of resources. Another important limitation, in the current literature for Bangla, is the absence of comparable results due to the lack of a well-defined train/test split. In this study, we explore several publicly available sentiment labeled datasets and designed classifiers using both classical and deep learning algorithms. In our study, the classical algorithms include SVM and Random Forest, and deep learning algorithms include CNN, FastText, and transformer-based models. We compare these models in terms of model performance and time-resource complexity. Our finding suggests transformer-based models, which have not been explored earlier for Bangla, outperform all other models. Furthermore, we created a weighted list of lexicon content based on the valence score per class. We then analyzed the content for high significance entries per class, in the datasets. For reproducibility, we make publicly available data splits and the ranked lexicon list. The presented results can be used for future studies as a benchmark.
BanglaASTE: A Novel Framework for Aspect-Sentiment-Opinion Extraction in Bangla E-commerce Reviews Using Ensemble Deep Learning
Department of Computer Science；Engineering Chittagong University of Engineering；Department of Information Technology Washington University of Science \& Technology 2900 Eisenhower Ave, Alexandria, VA 22314, United States；Department of Information Systems Central Michigan University 1200 S. Franklin St. Mount Pleasant, Mich. 48859
A Multi-Model Approach to English-Bangla Sentiment Classification of Government Mobile Banking App Reviews
Data Science Research Lab, Department of Statistics, University of Rajshahi（数据科学研究实验室，统计系，拉贾沙希大学）；Department of Statistics, Mawlana Bhashani Science and Technology University（统计系，穆拉纳·巴希安尼科学与技术大学）；Department of Statistics, University of Rajshahi（统计系，拉贾沙希大学）
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）
University of Liberal Arts Bangladesh (ULAB)（孟加拉国自由艺术大学）
Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English language. Despite being the sixth most widely spoken language in the world, Bangla still does not have a large and standard dataset. Because of this, recent research works in Bangla have failed to produce re…
University of California, Berkeley（加州大学伯克利分校）；Nankai University（南开大学）；Massachusetts Institute of Technology（麻省理工学院）；Didi Chuxing（滴滴出行）
Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate large-scale annotations on the target domain, domain adaptation (DA) provides an alternate solution by learning a transferable model from other labeled source domains. Existing multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources and the…
CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool
Ahsanullah University Of Science and Technology (AUST)（阿萨努拉科技大学（AUST））
University of Saarland（萨尔大学）；University of Chittagong（吉大港大学）
Detecting emotions from text is an extension of simple sentiment polarity detection. Instead of considering only positive or negative sentiments, emotions are conveyed using more tangible manner; thus, they can be expressed as many shades of gray. This paper manifests the results of our experimentation for fine-grained emotion analysis on Bangla text. We gathered and annotated a text corpus consisting of user comments from several Facebook groups regarding socio-economic and political issues, and we made efforts to extract the basic emotions (sadness, happiness, disgust, surprise, fear, anger)…
A Unified BERT-CNN-BiLSTM Framework for Simultaneous Headline Classification and Sentiment Analysis of Bangla News
Department of CCE, International Islamic University Chittagong（国际伊斯兰大学丘拉图加冈分校计算机工程系）；Department of ICE, Noakhali Science and Technology University（诺阿克利科学与技术大学信息工程系）；Department of CSE, Banladesh University of Engineering and Technology（孟加拉工程与技术大学计算机科学与工程系）；Dept. of CSE, IIUC, Chattogram, Bangladesh（国际伊斯兰大学丘拉图加冈分校计算机科学与工程系）；School of CSE, University of Southern Mississippi（密西西比大学南方分校计算机科学学院）；Department of CSE, Netrokona University（纳特罗克纳大学计算机科学与工程系）；mPower Social Enterprise（mPower社会企业）
Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms
Uttara University（乌拉拉大学）；National Institute of Technology（印度国家理工学院）；University of Oslo（奥斯陆大学）；Maharishi International University（玛赫西国际大学）；CCN University of Science & Technology（CCN科技大学）
BJIT Limited（BJIT有限公司）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
Text classification has been one of the earliest problems in NLP. Over time the scope of application areas has broadened and the difficulty of dealing with new areas (e.g., noisy social media content) has increased. The problem-solving strategy switched from classical machine learning to deep learning algorithms. One of the recent deep neural network architecture is the Transformer. Models designed with this type of network and its variants recently showed their success in many downstream natural language processing tasks, especially for resource-rich languages, e.g., English. However, these m…
The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it eas…
University of Charleston（查尔斯顿大学）；Apple Inc.（苹果公司）；Cerence B. V.（赛轮思公司）
Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Further…
HEC Montréal（蒙特利尔高等商学院）；Université de Sherbrooke（舍布鲁克大学）；GERAD（GERAD 运筹与决策分析研究组）；University of Neuchâtel（纳沙泰尔大学）；Ghent University（根特大学）；Vrije Universiteit Brussel（布鲁塞尔自由大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.
Sentiment analysis is attracting more and more attentions and has become a very hot research topic due to its potential applications in personalized recommendation, opinion mining, etc. Most of the existing methods are based on either textual or visual data and can not achieve satisfactory results, as it is very hard to extract sufficient information from only one single modality data. Inspired by the observation that there exists strong semantic correlation between visual and textual data in social medias, we propose an end-to-end deep fusion convolutional neural network to jointly learn text…
Identifying and understanding underlying sentiment or emotions in text is a key component of multiple natural language processing applications. While simple polarity sentiment analysis is a well-studied subject, fewer advances have been made in identifying more complex, finer-grained emotions using only textual data. In this paper, we present a Transformer-based model with a Fusion of Adapter layers which leverages knowledge from more simple sentiment analysis tasks to improve the emotion detection task on large scale dataset, such as CMU-MOSEI, using the textual modality only. Results show th…
LORIA UMR 7503（洛林信息学及其应用研究实验室）；University of Lorraine（洛林大学）
Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc. However, building a general purpose tool for doing sentiment analysis and emotion detection raises a number of issues, theoretical issues like the dependence to the domain or to the language but also pratical issues like the emotion representation for interoperability. In this paper we present our sentiment/emotion analysis tools, the way we propose to circumvent the di culties and th…
MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification
Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh（伊斯兰科技大学计算机科学与工程系，达卡，孟加拉国）；Department of Computer Science, University of Illinois Chicago, Chicago, Illinois, United States of America（伊利诺伊大学芝加哥分校计算机科学系，芝加哥，伊利诺伊州，美国）
University of Stirling（斯特林大学）；Edinburgh Napier University（爱丁堡龙比亚大学）；Nanyang Technological University（南洋理工大学）
Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge public perception. Multimodal sentiment analysis offers an innovative solution to computationally understand and harvest sentiments from videos by contextually exploiting audio, visual and textual cues. In this paper, we, firstly, present a first of its kind Persian multimodal dataset comprising more than 800 utterances, as a benchmark resource for re…
SPPU Pune University（浦那大学）；Orthogonal Research & Educational Laboratory（正交研究与教育实验室）
Literary artefacts are generally indexed and searched based on titles, meta data and keywords over the years. This searching and indexing works well when user/reader already knows about that particular creative textual artefact or document. This indexing and search hardly takes into account interest and emotional makeup of readers and its mapping to books. When a person is looking for a literary textual artefact, he/she might be looking for not only information but also to seek the joy of reading. In case of literary artefacts, progression of emotions across the key events could prove to be th…
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Bangladesh University of Engineering & Technology（孟加拉国工程技术大学）；University of Dhaka（达卡大学）；Jahangirnagar University（贾汉吉尔纳加尔大学）；Jagannath University（贾格纳特大学）
In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market doma…
This paper uses textual data contained in certified (q-graded) coffee reviews to predict corresponding scores on a scale from 0-100. By transforming this highly specialized and standardized textual data in a predictor space, we construct regression models which accurately capture the patterns in corresponding coffee bean scores.
Shandong University（山东大学）；National University of Singapore（新加坡国立大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Existing studies on multimodal sentiment analysis heavily rely on textual modality and unavoidably induce the spurious correlations between textual words and sentiment labels. This greatly hinders the model generalization ability. To address this problem, we define the task of out-of-distribution (OOD) multimodal sentiment analysis. This task aims to estimate and mitigate the bad effect of textual modality for strong OOD generalization. To this end, we embrace causal inference, which inspects the causal relationships via a causal graph. From the graph, we find that the spurious correlations ar…
Engineering and Technology Institute, University Center of Belo Horizonte (UNI-BH)（贝洛奥里藏特大学中心（UNI-BH）工程技术学院）；CEFET-MG（米纳斯吉拉斯联邦教育技术中心（CEFET-MG））；Federal University of Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学（UFMG））
This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part of the modern-day lives of millions of people. To achieve this goal, we apply state-of-the-art computational methods for (1) automatic emotion recognition from facial expressions, (2) extraction of modulations in the participants' speeches and (3) sen…
Dublin City University（都柏林城市大学）；Westlake University（西湖大学）；Trinity College Dublin（都柏林圣三一学院）
User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architecture or do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product to initialize representations, and ii) effici…
Emotions are physiological states generated in humans in reaction to internal or external events. They are complex and studied across numerous fields including computer science. As humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. Lack of facial expressions and voice modulations make detecting emotions from text a challenging problem. However, as humans increasingly communicate using text messaging applications, and digital agents gain popularity in our society, it is essential that these digital ag…
In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.
Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual data
International Business School, Zhejiang University（浙江大学国际商务学院）；Center for Data Science, Zhejiang University（浙江大学数据科学中心）；Centre for Statistics, Georg-August-Universität Göttingen（哥廷根大学统计中心）
Sharif University of Technology（谢里夫理工大学）；University of Guilan（吉兰大学）
Sentiment analysis refers to the use of natural language processing to identify and extract subjective information from textual resources. One approach for sentiment extraction is using a sentiment lexicon. A sentiment lexicon is a set of words associated with the sentiment orientation that they express. In this paper, we describe the process of generating a general purpose sentiment lexicon for Persian. A new graph-based method is introduced for seed selection and expansion based on an ontology. Sentiment lexicon generation is then mapped to a document classification problem. We used the K-ne…
An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinion…
Kharazmi University（哈拉兹米大学）；K. N. Toosi University（卡·诺·图西大学）
Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results …
