A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews

Sahu, Sandipan; Kumar, Raghvendra; MohdShafi, Pathan; Shafi, Jana; Kim, SeongKi; Ijaz, Muhammad Fazal

doi:10.3390/math10091568

Cited by 22 publications

(2 citation statements)

References 62 publications

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“…TextBlob and VADER have the advantage of not requiring training data, as it is a lexicon-based approach. Therefore, they have been popular tools for analyzing comments on social networks, such as tweets [4][5][6][7][8], youtube [9][10][11][12][13] or Reddit [14][15][16][17] comments. Although the lexicon-based approach is suitable for general use, its main limitation lies in its difficulty adapting to changing contexts and linguistic uses [18].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

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This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

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Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

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“…Ecommerce recommender using technical indicators have always been a crucial area of research [4] . However, in the field of e-commerce, user reviews, which are a common and informative feature, are rarely used for recommendation, as pointed out by research [5]. This led to the development of a recommendation system, in which several experiments have proven that analyzing the sentiment factor in user reviews is the most effective way to improve the accuracy of recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

E-commerce recommender system based on sentiment multi-label classification

Ao,

Zhang

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Text sentiment analysis and its incorporation into recommender systems have been study topics in the past few years. Recommendation accuracy can be increased by using sentiment elements. One of the most popular is dichotomous sentiment analysis. Nevertheless, the real emotions of users are diverse, and sentiment dichotomous classification is not sufficient to fully express user attitudes. To address this problem, this paper innovatively proposes the Sentiment Multilabel Classification Recommender System (SMCRS) model, which establishes a six-label sentiment classification module for text, and extracts relevant word features for each label by decomposing the sentence The label prediction is finally realized. Moreover, the model builds higher-order interactions between various features to realize the recommendation function by incorporating sentiment as well as user and item information. Finally, we tested the performance of SMCRS on the JD dataset, and the accuracy of the sentiment classification module is as high as 78.02%, while the AUC of the whole model is improved by 6.35%. This is a great improvement, and it also proves that sentiment multi-classification is very helpful for the performance improvement of recommender systems.

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