A Combination of Lexicon-Based and Classified-Based methods for Sentiment Classification based on Bert

Zhang, Jiaxin

doi:10.1088/1742-6596/1802/3/032113

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…In contrast, traditional methods such as SVM and Multinomial Naive Bayes show lower metrics. Zhang [19] conducted sentiment analysis research, amalgamating the advantages of lexicon-based and classification-based methods while addressing their shortcomings. Lexiconed BERT exhibited commendable performance in processing lengthy sentences, leading to the conclusion that the Lexiconed BERT approach effectively enhanced sentiment classification performance.…”

Section: Related Workmentioning

confidence: 99%

Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches

Ibadurrohman Irfan Fatani

2024

jes

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Rapid urbanization in the Jabodetabek region has led to an increased demand for public transportation. Responding to this need, the government has initiated the development of a new public transportation mode, namely the LRT Jabodebek. However, as a new public transportation mode, the LRT Jabodebek has both strengths and weaknesses in serving the community. Various public comments are expressed through social media platforms. To enhance service quality, it is crucial to pay attention to public comments. Therefore, a sentiment analysis is required to identify and delve into both positive and negative sentiments regarding the LRT Jabodebek service through comments on Twitter, Instagram, and Youtube. The methodology involves a combination of Lexicon-based, IndoBERT model, and BERTopic approaches to gain a deeper understanding of the Jabodebek LRT service trends. The study reveals that 55.9% of the 8,523 comments carry a negative sentiment, and the IndoBERT model achieves an accuracy of 85.97% in sentiment classification.

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Section: Related Workmentioning

confidence: 99%

Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches

Ibadurrohman Irfan Fatani

2024

jes

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“…There are two main models of sentiment classification. One is based on deep learning, and the other is based on dictionary [13]. The characteristics of the "tree hole" message include short text, large number of messages, and complex thoughts expressed.…”

Section: Model Selectionmentioning

confidence: 99%

Research on the Fluctuation Characteristics of Social Media Message Sentiment with Time Before and During the COVID-19 Epidemic

Guo

Lin

Huang

et al. 2021

Health Information Science

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Tree hole" refers to a social media formed after the death of a social media user, in which other users continue to leave messages due to emotional resonance. This paper focuses on exploring the fluctuation of emotions with time in a "tree hole" of social media such as Microblog, and provides ideas and support for suicide warning, rescue, and user portraits of patients with depression in the "tree hole". In this paper, the dataset of 2,356,066 messages captured from the "tree hole" Microblog with the "tree hole" agent (i.e., an AI program) and preprocessed. Subsequently, the effective dataset was labeled by a text sentiment analysis model based on BERT and BiLSTM, and accordingly the sentiment was scored. Then the scored data was visualized and analyzed in the time dimension. Finally, it was found that the sentiment of the "tree hole" messages reached a trough at 4:00 am and a peak around 8:00 am. In addition, the overall trend of "tree hole" sentiment has fluctuated downwards from Monday to Sunday. We have concluded that the sentiment of patients with depression fluctuates regularly at some special time points, and special events such as the outbreak of COVID-19 and so on, have a great impact on the emotions of patients with depression. Therefore, it is necessary to strengthen warning and intervention for those who has expressed thoughts of suicide at special points to prevent the spread and fermentation of suicidal emotions in the "tree hole" in time. In addition, the rescue volunteers for patients with depression as Tree Hole Rescue Team should make corresponding adjustments to the rescue strategy when special events occur. This research is of great significance for the emergency response of "tree hole" depressed users in major events such as COVID-19 epidemic.

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A Combination of Lexicon-Based and Classified-Based methods for Sentiment Classification based on Bert

Cited by 2 publications

References 3 publications

Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches

Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches

Research on the Fluctuation Characteristics of Social Media Message Sentiment with Time Before and During the COVID-19 Epidemic

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