Aspect Based Sentiment Analysis in Bangla Dataset Based on Aspect Term Extraction

Haque, Sabrina; Rahman, Tasnim; Shakir, Asif Khan; Arman, Md. Shohel; Biplob, Khalid Been Badruzzaman; Himu, Farhan Anan; Das, Dipta; Islam, Shariful

doi:10.1007/978-3-030-52856-0_32

Cited by 19 publications

(4 citation statements)

References 11 publications

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“…Table 11 gives an overview of lexicon-driven tools and techniques. Some of the other machine learning algorithms used for opinion mining are Random Forest, NBSVM [27], K-NN [28], CRF, SentiStrength, Linear regression, HMM [29], Bagging [30], etc. Some of the other available lexicon-based tools which HowNet Sentiment Analysis Word Library (HowNet-Senti), NRC Emotion Lexicon, OpinionFinder [31], NRC Hashtag Sentiment Lexicon [32], Dictionaries for Movies and Finance, Disasters on Social Media, Senti-CS [33], Lexica etc.…”

Section: Tools and Techniquesmentioning

confidence: 99%

Opinion Mining Using Multi-Dimensional Analysis

Biswas

Poornalatha

2023

IEEE Access

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This review addresses emerging literature in the eld of Opinion Mining with a particular emphasis on user-generated textual content. The focus is on the various tasks involved in Opinion Mining, which satises the comprehension of the essential criteria and methodologies that should be considered prior to embraking on the task. The paper provides an in-depth analysis of benchmarked datasets, widely-used feature sets, algorithms, techniques, open-source tools, challenges, real-world applications, and insights into various dimensions of Opinion Mining. The ndings have both theoretical and practical implications, as it highlights the signicance of Opinion Mining of textual content in comprehensive society. This review covers both technical and real-world knowledge and oers a comprehensive understanding of available open-source tools that are used in real-time.

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Section: Tools and Techniquesmentioning

confidence: 99%

Opinion Mining Using Multi-Dimensional Analysis

Biswas

Poornalatha

2023

IEEE Access

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“…Machine learning is another area widely seen in the ABSA domain to find the aspects of an opinionated text. It makes use of two main sub-categories in machine learning as supervised (including Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and K-Nearest Neighbor (KNN) algorithms) and unsupervised learning [21][22][23]. The recent advancements in ABSA have also seen many approaches based on deep neural networks.…”

Section: Approaches In Absamentioning

confidence: 99%

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

Haq,

Daudpota,

Imran

et al. 2023

CMC

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The Internet has become one of the significant sources for sharing information and expressing users' opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories. Hence, the study deals with two main sub-tasks in ABSA, named Aspect Category Detection (ACD) and Aspect Term Extraction (ATE). In the first sub-task, aspects categories are extracted using topic modeling and filtered by an oracle further, and it is fed to zero-shot learning as the prompts and the augmented text. The predicted categories are the input to find similar phrases curated with extracting meaningful phrases (e.g., Nouns, Proper Nouns, NER (Named Entity Recognition) entities) to detect the aspect terms. The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment (MAMS) dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.

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“…The CAE model especially generated the best F1-score on the 'Restaurant' and 'Cricket' datasets with 0.87 and 0.91 respectively. Based on the dataset provided by Rahman et al [15], Haque et al constructed the Bangla ABSA model [17]. The traditional supervised aspect classification machine learning approach was employed in this work with minimal preprocessing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results of this study were compared with four earlier Bengali ABSA based papers, [1], [2], [15], [17] and [18]. Table VI shows a comparison of our dataset to datasets used in their studies.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Samia¹,

Rajee²,

Hasan³

et al. 2022

IJACSA

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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.

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Aspect Based Sentiment Analysis in Bangla Dataset Based on Aspect Term Extraction

Cited by 19 publications

References 11 publications

Opinion Mining Using Multi-Dimensional Analysis

Opinion Mining Using Multi-Dimensional Analysis

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

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