The Sentiment Analysis and Sentiment Orientation Prediction for Hotel Based on BERT-BiLSTM Model

Du, Yu; Yong-chong, Wang; Wei, Kaiyue; Jia, Jingjing

doi:10.1007/978-981-16-9423-3_62

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Comparative experiments were conducted focused on MAE and Coverage based on the selected data sets. Three algorithms were selected as the reference to judge the performance of the algorithm, they are the traditional collaborative filtering recommendation algorithm, and the algorithm combined time factor proposed in reference [9], and recommendation strategy based on trust model via emotional analysis of online comment appeared in [14]. Results are shown in Figure 3 and Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we use the improved DP algorithm proposed in reference [14] to get aspect-level analysis of user personalized online comments. DP algorithm, named double propagation algorithm, is an effective method to mine objects and emotional words from online reviews based on certain language association syntactic rules.…”

Section: Analysis Of Emotional Tendency In User Commentsmentioning

confidence: 99%

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A personalized recommendation strategy based on emotional analysis and forgetting rule

Zhubing

xiaoqin²,

2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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In traditional collaborative filtering recommendation system, drawbacks such as interest drift and data sparseness always exist, which has weakened performance of the system. In this paper, a novel approach from the perspective of emotion analysis and forgotten rules was proposed to offset the influence on recommendation accuracy caused by interest migration. Firstly, user online comment has been analyzed to extract emotional words and the rated object using optimized DP algorithm. Secondly, the emotional words about the rated object have been appropriate quantified so that we can take advantage of the user emotional tendency to generate interest model. Thirdly, the Ebbinghaus forgetting curve is also introduced into the recommendation process to overcome the problem that caused by the change of user interest, which can effectively increase the accuracy and offer user better service when interest move. The simulation experiment shows that the strategy could greatly reduce MAE and effectively improve coverage.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Emotional Tendency In User Commentsmentioning

confidence: 99%