Sentiment Analysis on IMDB Movie Reviews using Machine Learning and Deep Learning Algorithms

Amulya, K.; Swathi, S.; Kamakshi, P.; Bhavani, Y.

doi:10.1109/icssit53264.2022.9716550

Cited by 20 publications

(3 citation statements)

References 9 publications

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“…However, literature utilizing sentiment corpus for sentiment analysis often overlooks the disruption that sarcasm words may cause in appraising sentiment words. Additionally, research examining film reviews using sentiment analysis techniques predominantly relies on IMDb data, i.e., English text [46][47][48][49][50]. Sentiment analysis based on Chinese corpus is an established method that has been applied in numerous contexts [51].…”

Section: Related Work and Hypothesismentioning

confidence: 99%

Online Evaluation Information Cascade and Its Impact on Consumer Decision Making: Analyzing Movie Reviews Using Sentiment Corpus

Tuo

2024

IEEE Access

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User-generated content on self-media platforms significantly influences the market. In the era of Web 2.0, consumers make purchasing decisions based on electronic word-of-mouth (eWOM) of these platforms. This research illustrates how sentiment value in eWOM content guides consumers' behavior by empirical study based on the causality approach. The research calculates the sentiment value of 160,000 textual film reviews using sentiment analysis program which is based on the sentiment corpus and addressing sarcasm. It also measures the complexity of information by calculating entropy to capture the information cascade process of eWOM into reputation signal. The findings illustrate the causal relationship between positive eWOM content and consumers' decision-making processes, as well as the extent to which consumers rely on eWOM textual information when making consumption decisions. When the eWOM information conveys a clear reputation signal, it will have a lasting impact on future box office performance. This article is the initial empirical literature that quantifies the commercial value of online text sentiment information through a causal and dynamic perspective. It is also the first literature to empirically demonstrate the formation process of sentiment information cascade during the eWOM spreading process among the netizens and capture the required time lag for the formation of online consumer reputation signal.INDEX TERMS Sentiment analysis; information cascade; entropy; user-generated content; Chinese text; movie reviews HONG TUO received the B.S. degree in information management and information system from Beijing University of Chemical and Technology, Beijing, in 2019. She is currently pursuing the Ph.D. degree in economics at

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Section: Related Work and Hypothesismentioning

confidence: 99%

Online Evaluation Information Cascade and Its Impact on Consumer Decision Making: Analyzing Movie Reviews Using Sentiment Corpus

Tuo

2024

IEEE Access

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“…Recently, research interest has increased significantly in sentiment analysis using new methods and algorithms based on deep learning, machine learning, and the use of transformers. Many algorithms, such as Naive Bayes, Decision Tree, KNN, SVM, and LSTM, as well as transformer-based algorithms, were applied to the IMDb dataset, which is a balanced sentiment dataset [7], [8], [9]. For various sentiment classification approaches, Prajval et al [10] provide a comparative study, and the technical and non-technical aspects and challenges of opinion mining and sentiment analysis are discussed in [11].…”

Section: Introductionmentioning

confidence: 99%

Language-Agnostic Method for Sentiment Analysis of Twitter

Jafari,

Farahbakhsh,

Salehi

et al. 2024

Lecture Notes in Networks and Systems

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With the different events and crises that we are witnessing these days, Twitter plays an essential role in sharing thoughts, opinions, and news worldwide in various languages. Understanding the sentiment of user-generated content has garnered much interest in both industrial and academic communities in recent studies. Due to the limited availability of data from low-resource languages, the focus on multilingual resources is a limiting and challenging issue of sentiment analysis task. Considering the importance of pre-processing in the implementation of a sentiment analysis system, we propose a method consisting of two steps for the pre-processing of tweets in different languages i) a language-agnostic step to replace or remove some elements in the Twitter data structure and ii) a text-normalization step based on the main high-resource language. In addition, we used machine translation techniques to translate low-resource language texts into the main language. We evaluated sentiment classification approaches based on four deep models: an RNN model and three BERT-based architectures namely vanilla-version, a languagespecific and a large-scale pre-trained model for Twitter. The results show that our method had better accuracy when using a large-scale BERT-based pre-trained model.

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“…The most recent statistical data exhibit to be more than 500 million tweets are transferred every day, creating a large quantity of social information that is employed by various higher-level analytical applications for making more benefits [2]. In the meantime, several analyses are implemented on Twitter information for creating natural language processing (NLP) applications namely, topic modelling, sentiment analysis (SA), relation extraction, named entity recognition (NER), and question and answer (Q&A) [3]. SA has been frequently called sentiment mining, which is a main element of NLP and aims to help users for detecting and analyzing the sentiments comprised in subjective texts.…”

Section: Introductionmentioning

confidence: 99%

Applied Linguistics With Red-Tailed Hawk Optimizer-Based Ensemble Learning Strategy in Natural Language Processing

Alshahrani,

Hassan,

Almalki

et al. 2023

IEEE Access

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Natural Language Processing (NLP) is the most vital technology in currently utilized, specifically caused by the huge and growing count of online texts that requires that understood for its massive value that completely asserted. NLP is create a sense of unstructured data, which are created by social networks and other social data sources, and is supported to organize them into an additional structured model that assists several kinds of tasks and applications. Sentiment analysis (SA), a subfield of NLP contains determining the sentiment expressed or emotional tone from a piece of text. Deep learning (DL) approaches are significantly advanced the field of SA, permitting for more accurate and nuanced classification of sentiments from the text data. In this article, we present an Advanced Sentiment Analysis using a Red-Tailed Hawk Optimizer with Ensemble Learning (ASA-RTHEL) Strategy in NLP. The aim of ASA-RTHEL technique is to exploit the strategies of ensemble learning with a hyperparameter tuning process for SA. The ASA-RTHEL technique mainly follows an ensemble learning-based classification process, which combines prediction from three DL approaches convolutional neural network (CNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The ensemble process results in enhanced SA performance and decreases the risk of depending only on a single model bias or error. To boost the SA performance, the hyperparameter tuning strategy is performed by the use of the RTH algorithm. An extensive set of experiments were carried out for ensuring the superior SA results of ASA-RTHEL technique. The comprehensive comparison study highlighted the enhanced results of the MPONLP-TSA method on the recognition of various kinds of sentiments.INDEX TERMS Sentiment analysis; Natural language processing; Red-Tailed Hawk Optimizer; Deep learning; Ensemble learning.

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Sentiment Analysis on IMDB Movie Reviews using Machine Learning and Deep Learning Algorithms

Cited by 20 publications

References 9 publications

Online Evaluation Information Cascade and Its Impact on Consumer Decision Making: Analyzing Movie Reviews Using Sentiment Corpus

Online Evaluation Information Cascade and Its Impact on Consumer Decision Making: Analyzing Movie Reviews Using Sentiment Corpus

Language-Agnostic Method for Sentiment Analysis of Twitter

Applied Linguistics With Red-Tailed Hawk Optimizer-Based Ensemble Learning Strategy in Natural Language Processing

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