Characterization of the discrepancies between scores and texts of movie reviews
Author:
Affiliation:
1. Universidade Federal de Minas Gerais
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3323503.3360296
Reference24 articles.
1. Exploring Unsupervised Learning Towards Extractive Summarization of User Reviews
2. iFeel
3. Peter Boatwright Suman Basuroy and Wagner Kamakura. 2007. Reviewing the reviewers: The impact of individual film critics on box office performance. Quantitative marketing and economics 5 4 (2007) 401--425. Peter Boatwright Suman Basuroy and Wagner Kamakura. 2007. Reviewing the reviewers: The impact of individual film critics on box office performance. Quantitative marketing and economics 5 4 (2007) 401--425.
4. Hui Du Xueke Xu Xueqi Cheng Dayong Wu Yue Liu and Zhihua Yu. 2016. Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW '16 Companion). International World Wide Web Conferences Steering Committee Republic and Canton of Geneva Switzerland 29--30. 10.1145/2872518.2889403 Hui Du Xueke Xu Xueqi Cheng Dayong Wu Yue Liu and Zhihua Yu. 2016. Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW '16 Companion) . International World Wide Web Conferences Steering Committee Republic and Canton of Geneva Switzerland 29--30. 10.1145/2872518.2889403
5. Convolutional Recurrent Deep Learning Model for Sentence Classification
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