Context-aware sentiment analysis with attention-enhanced features from bidirectional transformers

Sivakumar, S.; Rajalakshmi, Ratnavel

doi:10.1007/s13278-022-00910-y

Cited by 16 publications

(2 citation statements)

References 51 publications

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“…The ACC (Automatic conversation model in English context) model in this study introduces the methods of mask and next presence prediction, which enhances the understanding of context and sentence structure. Through the mask method, BERT can predict missing words in sentences; Through the next presence prediction method, BERT is able to identify the relationships between different parts of long and complex sentences [27]. In automatic conversations, BERT can recognize and understand long and difficult sentences using the above methods, making the automatic conversation model more accurate and reliable.…”

Section: B Automatic Recognition Of Long and Difficult Sentences In E...mentioning

confidence: 99%

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

Li,

Liu,

Zhang

et al. 2024

IEEE Access

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The contextual understanding ability in complex conversation scenarios has been a challenging issue, and existing methods mostly failed to possess such characteristics. To bridge such gap, this paper formulates a novel composite large language model to investigate such issue. As a result, taking English context as the scene, a Transformer-BERT integrated model-based automatic conversation model is proposed in this work. Firstly, the unidirectional BERT-based automatic conversation model is improved by introducing attention mechanism. It is expected to enhance feature expression for conversation texts by linking context to identify long-difficult sentences. Besides, a bidirectional Transformer encoder is utilized as the input layer before the BERT encoder. Through the two modules, dynamic language training based on English situational conversations can be completed to build the automatic conversation model. The proposed conversation model is further assessed on massive real-world English language context in terms of conversation performance. The experimental results show that compared with traditional rule-based or machine learning methods, the proposal has significantly improved response quality and fluency in English context. It can more accurately understand context, capture subtle semantic differences, and generate more coherent responses.

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Section: B Automatic Recognition Of Long and Difficult Sentences In E...mentioning

confidence: 99%

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

Li,

Liu,

Zhang

et al. 2024

IEEE Access

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show abstract

“…BERT models have been reported to produce state-of-the-art results (Devlin et al, 2018;Feng et al, 2022;Liu et al, 2019) for general NLP tasks. Specifically, in sentiment/emotion analysis, BERTbased methods (Martin et al, 2021;Sivakumar & Rajalakshmi, 2022) report significant gains over convolutional, recurrent, and traditional machine learning methods. This has led us to incorporate BERT-based methods into sentiment analysis in the current study.…”

Section: Different Computational Approaches To Sentiment Analysismentioning

confidence: 99%

Automated Measures of Sentiment via Transformer- and Lexicon-Based Sentiment Analysis (TLSA)

Zhao¹,

Wong²

2023

Preprint

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<p>The last decade witnessed the proliferation of automated content analysis in communication research. However, existing computational tools have been taken up unevenly, with powerful deep learning algorithms such as transformers rarely applied as compared to lexicon-based dictionaries. To enable communication scholars to adopt modern computational methods for valid and reliable sentiment analysis, we propose a free and open web service named transformer- and lexicon-based sentiment analysis (TLSA). TLSA integrates different sentiment analysis tools including transformer-based deep learning models and sentiment dictionaries. TLSA enables users with limited computational resources to use these tools and validate their results at once. Two cases showed that transformer-based models provided more accurate measurement of sentiments in tweets, compared to lexicon-based methods. The performance of different tools varied to a large extent based on the dataset, suggesting the importance to validate different sentiment tools in a specific study. TLSA is expected to empower communication scholars to reap the benefit of state-of-the-art computational methods for valid measurement and insight discovery. </p>

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MMOD-MEME: A Dataset for Multimodal Face Emotion Recognition on Code-Mixed Tamil Memes

Kannan

Ravikiran

Rajalakshmi

2023

Communications in Computer and Information Science

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Context-aware sentiment analysis with attention-enhanced features from bidirectional transformers

Cited by 16 publications

References 51 publications

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

Automated Measures of Sentiment via Transformer- and Lexicon-Based Sentiment Analysis (TLSA)

MMOD-MEME: A Dataset for Multimodal Face Emotion Recognition on Code-Mixed Tamil Memes

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