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Relevant bibliographies by topics / IMDb DATASET

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Academic literature on the topic 'IMDb DATASET'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "IMDb DATASET"

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Jung, Soon-Gyo, Joni Salminen, and Bernard J. Jansen. "Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment: Analyzing the Sentiment Bias of Four Major Tools." Proceedings of the ACM on Human-Computer Interaction 6, EICS (2022): 1–20. http://dx.doi.org/10.1145/3532203.

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Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (M

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Jnoub, Nour, Fadi Al Machot, and Wolfgang Klas. "A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models." Applied Sciences 10, no. 18 (2020): 6221. http://dx.doi.org/10.3390/app10186221.

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Most people nowadays depend on the Web as a primary source of information. Statistical studies show that young people obtain information mainly from Facebook, Twitter, and other social media platforms. By relying on these data, people may risk drawing the incorrect conclusions when reading the news or planning to buy a product. Therefore, systems that can detect and classify sentiments and assist users in finding the correct information on the Web is highly needed in order to prevent Web surfers from being easily deceived. This paper proposes an intensive study regarding domain-independent cla

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Kamaru Zaman, Fadhlan Hafizhelmi. "Gender classification using custom convolutional neural networks architecture." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5758. http://dx.doi.org/10.11591/ijece.v10i6.pp5758-5771.

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Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some pop

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Alghazzawi, Daniyal M., Anser Ghazal Ali Alquraishee, Sahar K. Badri, and Syed Hamid Hasan. "ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review." Sustainability 15, no. 9 (2023): 7076. http://dx.doi.org/10.3390/su15097076.

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Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classificat

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Effendi, Fery Ardiansyah, and Yuliant Sibaroni. "Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 5 (2021): 863–75. http://dx.doi.org/10.29207/resti.v5i5.3400.

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Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of ran

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Aribowo, Agus Sasmito, Halizah Basiron, and Noor Fazilla Abd Yusof. "Semi-supervised learning for sentiment classification with ensemble multi-classifier approach." International Journal of Advances in Intelligent Informatics 8, no. 3 (2022): 349. http://dx.doi.org/10.26555/ijain.v8i3.929.

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Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, ve

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Shaddeli, Aitak, Farhad Soleimanian Soleimanian Gharehchopogh, Mohammad Masdari, and Vahid Solouk. "An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews." Big Data and Cognitive Computing 6, no. 4 (2022): 104. http://dx.doi.org/10.3390/bdcc6040104.

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The African vulture optimization algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-st

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Zhou, Yancong, Qian Zhang, Dongdong Wang, and Xiaoying Gu. "Text Sentiment Analysis Based on a New Hybrid Network Model." Computational Intelligence and Neuroscience 2022 (December 28, 2022): 1–15. http://dx.doi.org/10.1155/2022/6774320.

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The research of text sentiment analysis based on deep learning is increasingly rich, but the current models still have different degrees of deviation in understanding of semantic information. In order to reduce the loss of semantic information and improve the prediction accuracy as much as possible, the paper creatively combines the doc2vec model with the deep learning model and attention mechanism and proposes a new hybrid sentiment analysis model based on the doc2vec + CNN + BiLSTM + Attention. The new hybrid model effectively exploits the structural features of each part. In the model, the

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Gore, Mohini, Aishwarya Sheth, Samrudhi Abbad, Paryul Jain, and Prof Pooja Mishra. "IMDB Box Office Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2438–42. http://dx.doi.org/10.22214/ijraset.2022.42653.

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Abstract: Movies are a big part of our world! But nobody knows how a movie will perform at the box office. There are some bix budget movies that bomb and there are smaller movies that are smashing successes. This project tries to predict the overall worldwide box office revenue of movies using data such as the movie cast, crew, posters, plot keywords, budget, production companies, release dates, languages, and countries. The dataset on Kaggle contains all these data points that you can use to predict how a movie will fare at the box office. Among many movies that have been released, some gener

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Abdullah Haje, Umran, Mohammed Hussein Abdalla, Reben Mohammed Saleem Kurda, and Zhwan Mohammed Khalid. "A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning." Academic Journal of Nawroz University 11, no. 1 (2022): 130–40. http://dx.doi.org/10.25007/ajnu.v11n1a1250.

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Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learni

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Dissertations / Theses on the topic "IMDb DATASET"

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YADAV, DEEPIKA. "SENTIMENT ANALYSIS ON TWITTER DATA." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18821.

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Prior to purchasing an item, individuals for the most part go to different shops in the market, question about the item, cost, and guarantee, and afterward at long last purchase the item dependent on the feelings they got on cost and nature of administration. This procedure is tedious and the odds of being cheated by the merchant are more as there is no one to direct regarding where the purchaser can get valid item and with legitimate expense. Be that as it may, presently a-days a decent number of people rely upon the upon line showcase for purchasing their necessary items. This is on the grou

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Book chapters on the topic "IMDb DATASET"

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Yousuf, Saad Bin, Hasan Sajid, Simon Poon, and Matloob Khushi. "IMDB-Attire: A Novel Dataset for Attire Detection and Localization." In Neural Information Processing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_46.

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Feng, Wenying, Daren Zha, Lei Wang, and Xiaobo Guo. "IMDb30: A Multi-relational Knowledge Graph Dataset of IMDb Movies." In Knowledge Science, Engineering and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_53.

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Balci, Salih, Gozde Merve Demirci, Hilmi Demirhan, and Salih Sarp. "Sentiment Analysis Using State of the Art Machine Learning Techniques." In Digital Interaction and Machine Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_3.

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AbstractSentiment analysis is one of the essential and challenging tasks in the Artificial Intelligence field due to the complexity of the languages. Models that use rule-based and machine learning-based techniques have become popular. However, existing models have been under-performing in classifying irony, sarcasm, and subjectivity in the text. In this paper, we aim to deploy and evaluate the performances of the State-of-the-Art machine learning sentiment analysis techniques on a public IMDB dataset. The dataset includes many samples of irony and sarcasm. Long-short term memory (LSTM), bag of tricks (BoT), convolutional neural networks (CNN), and transformer-based models are developed and evaluated. In addition, we have examined the effect of hyper-parameters on the accuracy of the models.

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Pachore, A. B., and R. Remesan. "Spatio-Temporal Analysis of Meteorological Drought Using IMD 0.25° Gridded Dataset for Marathwada Region." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0304-5_18.

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Krishnan, R., J. Sanjay, Chellappan Gnanaseelan, Milind Mujumdar, Ashwini Kulkarni, and Supriyo Chakraborty. "Correction to: Assessment of Climate Change over the Indian Region." In Assessment of Climate Change over the Indian Region. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-4327-2_13.

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In the original version of the book, belated corrections as listed below are incorporated: Chapter 1: The caption of figure 1.5 has been changed as follows: Spatial patterns of change in the June–to-September seasonal precipitation (mm day −1) over the globe in the left-hand column, and over India in the right-hand column. In the top row are plotted the observed changes for the period (1951-2014) relative to (1900-1930) over the globe based on the CRU dataset, and over India based on the IMD dataset. Plots in the middle row are from the IITM-ESM simulations for the historical period, and the plots in the last row are from the IITM-ESM projections following the SSP5-8.5 scenario. The IITM-ESM simulated changes in the historical period (first and middle rows) are plotted as difference for the period (1951-2014) relative to (1850–1900). Changes under the SSP5-8.5 scenario (last row) are plotted as difference between the far-future (2070–2099) relative to (1850–1900). Chapter 2: On page 40, line 5 the word “business-as-usual” has been changed to “twenty-first century under this high emission scenario”. Chapter 4: On page 88, the last sentence “The business as usual scenario will continue to increase atmospheric CO2 and CH4 loading for next several decades.” has been changed to “Without rapid mitigation policies, atmospheric CO2 and CH4 loading will continue to increase for the next several decades.” The erratum chapters and book have been updated with the changes.

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Conference papers on the topic "IMDb DATASET"

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Tripathi, Sandesh, Ritu Mehrotra, Vidushi Bansal, and Shweta Upadhyay. "Analyzing Sentiment using IMDb Dataset." In 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2020. http://dx.doi.org/10.1109/cicn49253.2020.9242570.

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Sikhi, Yalavarthi, S. Anjali Devi, Sreekar Kumar Jasti, and M. Sitha Ram. "Sentimental Analysis through Speech and text for IMDB Dataset." In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2022. http://dx.doi.org/10.1109/icssit53264.2022.9716303.

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Mathapati, Savitha, Amulya K. Adur, R. Tanuja, S. H. Manjula, and K. R. Venugopal. "Collaborative Deep Learning Techniques for Sentiment Analysis on IMDb Dataset." In 2018 Tenth International Conference on Advanced Computing (ICoAC). IEEE, 2018. http://dx.doi.org/10.1109/icoac44903.2018.8939068.

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Butler, Martin, and Stefan Robila. "Interface for querying and data mining for the IMDb dataset." In 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2016. http://dx.doi.org/10.1109/lisat.2016.7494103.

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Künas, Cristiano Alex, Leandro Perius Heck, and Edson Luiz Padoin. "Implementação de Rede Neural Artificial em Plataforma GPU Aplicada na Análise de Sentimentos em Textos." In Escola Regional de Alto Desempenho da Região Sul. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eradrs.2020.10751.

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Este artigo apresenta uma proposta de paralelização de uma Rede Neural Artificial em Plataforma GPU para aplicação na análise da polaridade de sentimentos expressado em textos e/ou postagens. Na implementação será utilizado Redes Neurais Recorrentes do tipo Long Short-Term Memory uma vez que Redes Neurais Artificiais podem auxiliar na extração automática de sentimentos ou sensação de sentenças. Com a aplicação da proposta em caso reais a partir do treinamento com o IMDb Review Dataset, que possui 50.000 registros espera-se uma boa precisão nos resultados.

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Попова, Екатерина, Ekaterina Popova, Владимир Спицын, Vladimir Spicyn, Юлия Иванова, and Yuliya Ivanova. "Using artificial neural networks to solve text classification problems." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-270-273.

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The article is devoted to neural network text classification algorithms. The relevance of this topic is due to the ever-growing volume of information on the Internet and the need to navigate it. In this paper, in addition to the classification algorithm, a description is also given of the methods of text preprocessing and vectorization, these steps are the starting point for most NLP tasks and make neural network algorithms efficient on small data sets. In the work, a sampling of 50,000 English IMDB movie reviews will be used as a dataset for training and testing the neural network. To solve t

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Bal, Malyaban, and Abhronil Sengupta. "Sequence Learning Using Equilibrium Propagation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/329.

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Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation. The effectiveness of EP stems from the fact that it relies only on local computations and requires solely one kind of computational unit during both of its training phases, thereby enabling greater applicability in domains such as bio-inspired neuromorphic computing. The dynamics of the model in EP is governed by an energy function and the internal states of the model consequently converge to a steady state following the state transition rules defined by th

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Krishna, Muddada Murali, Balaganesh Duraisamy, and Jayavani Vankara. "Hybrid Deep Learning Techniques for Sentiment Analysis on IMDB Datasets." In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2022. http://dx.doi.org/10.1109/icacite53722.2022.9823898.

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La Malfa, Emanuele, Rhiannon Michelmore, Agnieszka M. Zbrzezny, Nicola Paoletti, and Marta Kwiatkowska. "On Guaranteed Optimal Robust Explanations for NLP Models." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/366.

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We build on abduction-based explanations for machine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the input text that satisfies two key features: optimality w.r.t. a user-defined cost function, such as the length of explanation, and robustness, in that they ensure prediction invariance for any bounded perturbation in the embedding space of the left-out words. We present two solution algorithms, respectively based on implicit hitting sets and maximum universal sub

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Song, Kaisong, Wei Gao, Shi Feng, Daling Wang, Kam-Fai Wong, and Chengqi Zhang. "Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/382.

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Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-d

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