Heuristic-Assisted BERT for Twitter Sentiment Analysis

Yenduri, Gokul; Rajakumar, B. R.; Praghash, K.; Binu, D.

doi:10.1142/s1469026821500152

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…In addition, we used random sampling methods to interview 20 students after the course in order to understand the students' attitude towards this course. Only one student expressed his feelings that online mobile devices are too entertaining and often cannot concentrate on learning [15,16]. He believes that the teaching effect of this model is not effective, so he is not satisfied.…”

Section: 42mentioning

confidence: 99%

College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future

Zhang

2022

Wireless Communications and Mobile Computing

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With the global integration and the development of science and technology, higher requirements have been put forward for English teaching in China. Society urgently needs English talents with high English cross-cultural communication ability and strong self-learning ability. The concept of “Teaching for the Future” (TTF) advocates the use of technology as a tool for teaching and learning. In the context of the “big data” era, the rapid development of network information technology has spawned a series of new educational models. Based on the SPOC teaching model, this paper constructs a new cross-cultural teaching model for college English and conducts experiments on two selected classes of the same grade in a university. The experimental results show that students increase their interest in learning English culture and enhance their ability to communicate and collaborate. Therefore, the integration of future education and network technology represented by SPOC will play a certain role in promoting the cross-cultural teaching of English in colleges and universities.

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Section: 42mentioning

confidence: 99%

College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future

Zhang

2022

Wireless Communications and Mobile Computing

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“…(1) BiGRU [25]: Regardless of the contextual information in the conversation, it treats each utterance as an independent instance and uses a bidirectional GRU to encode the utterance and classify sentiment. (2) BERT [26]: This model is used to construct the utterance representations which are sent to a two-layer perceptron with a final SoftMax layer for sentiment classification. (3) ERNIE [22]: This model treats each sentence in the dialogue as an independent instance and uses the ERNIE model to encode the sentence and classify sentiment.…”

Section: Baselines Methodsmentioning

confidence: 99%

Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

Huang

Liu

Zhu³

et al. 2023

Applied Sciences

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In the e-commerce environment, conversations between customers and businesses contain a multitude of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. Contextual semantics information and inter-speaker affective interaction information are two key factors for identifying customers’ sentiments in conversation. Unfortunately, none of the existing approaches consider the two factors simultaneously. In this paper, we propose a conversational sentiment analysis method based on contextual semantic and affective interaction information. The proposed approach uses different bidirectional gated recurrent unit (BiGRU) combined with attention mechanisms to encode the contextual semantic information of different types of conversational texts. For modeling affective interactions, we use directed graph structures to portray the affective interactions between speakers and encode them with affective interaction features using graph convolutional neural networks (GCN). Finally, the two features are afused to recognize customer sentiment. The experimental results on the JDDC dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.

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“…is pre-trained on unprocessed English text ( Devlin et al, 2018 ) hence the only processing performed was to remove tweets that consisted of only hyperlinks. A more detailed discussion of our data collection platform and its combination with can be found in ( Qudar & Mago, 2020 ), while an abundance of studies show how is used for classification of tweets ( Singh, Jakhar & Pandey, 2021 ; Yenduri et al, 2021 ; Sadia & Basak, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Galgoczy

Phatak

Vinson

et al. 2022

PeerJ Computer Science

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Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets (via BERT) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

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Heuristic-Assisted BERT for Twitter Sentiment Analysis

Cited by 10 publications

References 32 publications

College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future

College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future

Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

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