Attention-based convolutional neural network for Bangla sentiment analysis

Sharmin, Sadia; Chakma, Danial

doi:10.1007/s00146-020-01011-0

Cited by 29 publications

(8 citation statements)

References 25 publications

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“…Figures [8][9][10][11] show all comparative experiments and the experimental results of the method in this paper on the correct rate, recall rate, F 1 value and average results of positive, neutral and negative blog posts. e performance of method 3 is the most unsatisfactory.…”

Section: Comparison Of Methodsmentioning

confidence: 99%

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Multimodal Blog Sentiment Classification Based on MD-HFCE

Yang

Tian

2021

Security and Communication Networks

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In recent years, the rapid growth of multimodal information has become an important factor affecting the results of sentiment analysis. However, a few state-of-the-art works take into account the multimodal features and sentiment fuzziness. To this end, a fuzzy method is proposed for assessing sentiment intensity in this paper. Firstly, based on the visual-text conversion network (CNN-LSTM), as well as sentiment optimization through SentiBank and SentiBridge, the visual features are normalized to the text features. At the same time, the emotional features of the extracted audio will be predicted by the random forest algorithm. Subsequently, the sentiment characteristics are processed by dual hesitant fuzzification to form positive and negative sentiment intensity factors. Finally, a classification method, that is, MD-HFCE (multilayer dual hesitant fuzzy comprehensive evaluation), fuzzy comprehensive evaluation method improved by Mamdani fuzzy reasoning, is proposed to realize the multifeature fuzzy sentiment classification based on the comprehensive sentiment dictionary. The classification results are applicable to the topics of sentiment monitoring. The experimental results show that the proposed algorithm can effectively realize feature integration and improve the average sentiment classification accuracy of multimodal blogs to 82.2%.

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Section: Comparison Of Methodsmentioning

confidence: 99%

“…CNN can effectively integrate multimodal information [11,12], which improves the sentiment analysis of the long text by increasing the convolutional layers [13]. In addition, the LSTM network pays attention to the semantic environment where the sentiment words are located [14].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Blog Sentiment Classification Based on MD-HFCE

Yang

Tian

2021

Security and Communication Networks

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“…Both corpus labelled into positive and negative sentiment polarities. A recent study developed a dataset for sentiment analysis which contained 2979 Bengali reviews and comments [ 37 ]. This dataset was annotated with positive, negative, and neutral polarities.…”

Section: Related Workmentioning

confidence: 99%

BEmoC: A Corpus for Identifying Emotion in Bengali Texts

et al. 2022

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Emotion classification in text has growing interest among NLP experts due to the enormous availability of people’s emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications. However, It is a very critical task to develop an automatic emotion classification system for low-resource languages such as, Bengali. Scarcity of resources and deficiency of benchmark corpora make the task more complicated. Thus, the development of a benchmark corpus is the prerequisite to develop an emotion classifier for Bengali texts. This paper describes the development of an emotional corpus (hereafter called ‘BEmoC’) for classifying six emotions in Bengali texts. The corpus development process consists of four key steps: data crawling, pre-processing, labelling, and verification. A total of 7000 texts are labelled into six basic emotion categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with 0.969 Cohen’s κ score indicates the close agreement between the corpus annotators and the expert. The analysis of evaluation also represents that the distribution of emotion words obeys Zipf’s law. Moreover, the results of BEmoC analysis shown in terms of coding reliability, emotion density, and most frequent emotion words, respectively.

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“…Existing datasets for sentiment analysis for a low-resource language like Bangla suffer from three major limitations: 1) none to slight inter annotator agreement score questioning the annotation reliability (e.g., 0.11 in Ashik et al, 2019 and0.18 in Islam et al, 2020), 2) lack of cross-domain generalization capability due to large domain dependency (Wahid et al, 2019;Rahman et al, 2019;Sazzed, 2020), and 3) lack of public availability for further research (Karim et al, 2020;Nabi et al, 2016;Hassan et al, 2016;Sharmin and Chakma, 2020;Choudhary et al, 2018;Das and Bandyopadhyay, 2009).…”

Section: Introductionmentioning

confidence: 99%

SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts

Islam¹,

Kar²,

Islam³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts. This dataset comprises public comments on news and videos collected from social media covering 13 different domains, including politics, education, and agriculture. These comments are labeled with one of the polarity labels, namely positive, negative, and neutral. One significant characteristic of the dataset is that each of the comments is noisy in terms of the mix of dialects and grammatical incorrectness. Our experiments to develop a benchmark classification system show that hand-crafted lexical features provide superior performance than neural network and pretrained language models. We have made the dataset and accompanying models presented in this paper publicly available at https://git.

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Attention-based convolutional neural network for Bangla sentiment analysis

Cited by 29 publications

References 25 publications

Multimodal Blog Sentiment Classification Based on MD-HFCE

Multimodal Blog Sentiment Classification Based on MD-HFCE

BEmoC: A Corpus for Identifying Emotion in Bengali Texts

SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts

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