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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Box-office forecasting based on sentiments of movie reviews and Independent subspace method
Minhoe Hur, Pilsung Kang, Sungzoon Cho
Information Sciences (2016) Vol. 372, pp. 608-624
Closed Access | Times Cited: 85

Showing 26-50 of 85 citing articles:

Artificial intelligence-based predictions of movie audiences on opening Saturday
Yongdae An, Jinwon An, Sungzoon Cho
International Journal of Forecasting (2020) Vol. 37, Iss. 1, pp. 274-288
Closed Access | Times Cited: 16
Forecasting the box offices of movies coming soon using social media analysis: A method based on improved Bass models
Chuan Zhang, Yu‐Xin Tian, Zhi‐Ping Fan
Expert Systems with Applications (2021) Vol. 191, pp. 116241-116241
Closed Access | Times Cited: 14
Does Twitter chatter matter? Online reviews and box office revenues
Sunčica Vujić, Xiaoyu Zhang
Applied Economics (2018) Vol. 50, Iss. 34-35, pp. 3702-3717
Open Access | Times Cited: 16
A novel approach for product competitive analysis based on online reviews
Zhen He, Zheng Lu, Shuguang He
Electronic Commerce Research (2022) Vol. 23, Iss. 4, pp. 2259-2290
Closed Access | Times Cited: 9
Predicting the performance of TV series through textual and network analysis: The case of Big Bang Theory
Andrea Fronzetti Colladon, Maurizio Naldi
PLoS ONE (2019) Vol. 14, Iss. 11, pp. e0225306-e0225306
Open Access | Times Cited: 14
Electronic word-of-mouth effects on studio performance leveraging attention-based model
Yang Liu, Hao Fei, Qingguo Zeng, et al.
Neural Computing and Applications (2020) Vol. 32, Iss. 23, pp. 17601-17622
Closed Access | Times Cited: 14
Investigating the effects of textual reviews from consumers and critics on movie sales
Tianjie Deng
Online Information Review (2020) Vol. 44, Iss. 6, pp. 1245-1265
Closed Access | Times Cited: 14
Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model
Daekook Kang
Electronic Commerce Research (2021) Vol. 21, Iss. 1, pp. 41-72
Closed Access | Times Cited: 12
Sentiment Analysis Of Movie Review Using Machine Learning
Anshika Gupta, Sweta Pandey, Mandakini Priyadarshani Behera, et al.
(2024)
Open Access | Times Cited: 1
SOCIAL MEDIA ANALYTICS: VALUE CREATION WITH SENTIMENT ANALYSIS
Burcu AKIN, Umman Tuğba Gursoy
Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi (2018) Vol. 5, Iss. 3, pp. 797-811
Open Access | Times Cited: 14
Predicting rating polarity through automatic classification of review texts
Gregorius Satia Budhi, Raymond Chiong, Ilung Pranata, et al.
(2017), pp. 19-24
Closed Access | Times Cited: 11
The Effect of Online Reviews on Movie Box Office Sales
Li‐Chen Cheng, Yiwen Yang
Journal of Global Information Management (2022) Vol. 30, Iss. 1, pp. 1-16
Open Access | Times Cited: 7
The making of box-office collection: qualitative insights from Bollywood
Vijaya Patil, Hema Date, Satish Kumar, et al.
Marketing Intelligence & Planning (2022) Vol. 40, Iss. 8, pp. 1010-1023
Closed Access | Times Cited: 7
Revisiting predictions of movie economic success: random Forest applied to profits
Thaís Luiza Donega e Souza, Marislei Nishijima, Ricardo Pires
Multimedia Tools and Applications (2023) Vol. 82, Iss. 25, pp. 38397-38420
Open Access | Times Cited: 3
Structured low-rank inverse-covariance estimation for visual sentiment distribution prediction
An-An Liu, Yingdi Shi, Peiguang Jing, et al.
Signal Processing (2018) Vol. 152, pp. 206-216
Closed Access | Times Cited: 9
Analysis of sentiment based movie reviews using machine learning techniques
Sachin Chirgaiya, Deepak Sukheja, Niranjan Shrivastava, et al.
Journal of Intelligent & Fuzzy Systems (2021) Vol. 41, Iss. 5, pp. 5449-5456
Closed Access | Times Cited: 8
Sentiment Analysis Using Machine Learning Approaches
Ayushi Mitra, Sanjukta Mohanty
Advances in intelligent systems and computing (2021), pp. 63-68
Closed Access | Times Cited: 7
Mining Tourists’ Opinions on Popular Indian Tourism Hotspots using Sentiment Analysis and Topic Modeling
Shefali Singh, Tureen Chauhan, Vibhas Wahi, et al.
2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (2021)
Closed Access | Times Cited: 7
Risk sensitive scheduling strategies of production studios on the US movie market: An agent-based simulation
Francesco Bertolotti, Sabin Roman
Intelligenza Artificiale (2022) Vol. 16, Iss. 1, pp. 81-92
Closed Access | Times Cited: 5
Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries
Marco Del Vecchio, Alexander A. Kharlamov, Glenn Parry, et al.
Journal of the Operational Research Society (2020) Vol. 72, Iss. 5, pp. 1110-1137
Open Access | Times Cited: 7
Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases
Yi-Ting Huang, Ping‐Feng Pai
Symmetry (2020) Vol. 12, Iss. 4, pp. 625-625
Open Access | Times Cited: 7
What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews
Frank M. Schneider, Emese Domahidi, Felix Dietrich
Media and Communication (2020) Vol. 8, Iss. 3, pp. 153-163
Open Access | Times Cited: 7
Analyzing Movie Scripts as Unstructured Text
Seong-Ho Lee, Hye-Yeon Yu, Yun-Gyung Cheong
(2017), pp. 249-254
Closed Access | Times Cited: 5
Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions
Xiaozhong Lyu, Cuiqing Jiang, Yong Ding, et al.
Sustainability (2019) Vol. 11, Iss. 3, pp. 913-913
Open Access | Times Cited: 5
Deep Learning Approach in Sentiment Analysis: A Review
Enas A. H. Khalil, Enas M.F. El Houby, Hoda K. Mohamed
(2020), pp. 1-10
Closed Access | Times Cited: 5

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