Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture

Zhang, Qi; Zhang, Zufan; Yang, Mei; Zhu, Lianxiang

doi:10.1155/2021/6630811

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…The architecture used a CNN-BiLSTM-Attention network. The results on two existing datasets show that the proposed method can improve the accuracy of sentiment analysis [45]. Li et al designed a sentiment analysis method for Chinese stock reviews based on BERT that extracted features manually.…”

Section: Binary Sentiment Classificationmentioning

confidence: 99%

Deep Emotion Detection Sentiment Analysis of Persian Literary Text

Khodaei

Bastanfard

Saboohi

et al. 2022

Preprint

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A large amount of training data is needed to achieve accurate results in processing deep learning models. Although deep learning has played a successful role in sentiment analysis of texts, a new era in natural language processing has begun with the rise of pre-trained language models. Pre-trained models provide access to a large amount of training data. Emotion analysis in Persian text is challenging because, besides being one of the low resource languages, it needs more accurate methods for processing texts. In this study, a dataset that includes the emotional sentences of Persian literary texts was created. We compared two different methods for analyzing the emotion of Persian sentences. The first method uses the FastText and the Bi-LSTM model for emotion classification. In the second approach, we employ the XLM-R based on a deep bidirectional transformer to extract features from text and apply the Catboost algorithm to guide the model to focus on the most relevant class. The proposed model can be used in all languages, especially low-resource languages and imbalanced datasets. According to the experimental results, the proposed model achieved better binary classification results and employing the XLM-R, and Catboost algorithm yielded remarkable improvement in the multi-classification accuracy.

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Section: Binary Sentiment Classificationmentioning

confidence: 99%

Deep Emotion Detection Sentiment Analysis of Persian Literary Text

Khodaei

Bastanfard

Saboohi

et al. 2022

Preprint

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“…Figure 3, 4, 5 presents it clearly that the outcomes of HF-CSA are better in comparison with baseline systems over informal, anglicized and Crosslingual contents. Existing Literature of deep learning methods, Urdu transliterations systems [41], [42], [43], [44], [45], [46], [47], [48] and experimental setup revealed that unsupervised lexicon based systems generate satisfactory outcomes for standard, formal, informal as well as multilingual text of resource poor languages.…”

Section: Sensitivity = Tp (Tp + Fn )mentioning

confidence: 99%

Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

et al. 2023

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Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on the Twitter dataset and the robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively.INDEX TERMS Sentiment analysis, lexicon based methods, Urdu language processing, crosslingual orientation.

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“…In addition, hybrid CNN-LSTM models are applied for sentiment analysis on movie reviews by Rehman et al [30]. e same techniques are used in several works, for example, [29,[38][39][40]. Kaur et al [41] designed an algorithm called a hybrid heterogeneous support vector machine (H-SVM).…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Models for Sentiment Analysis

2021

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Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

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Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture

Cited by 10 publications

References 39 publications

Deep Emotion Detection Sentiment Analysis of Persian Literary Text

Deep Emotion Detection Sentiment Analysis of Persian Literary Text

Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

Hybrid Deep Learning Models for Sentiment Analysis

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