Effects of Sentiment Analysis on Feedback Loops between Different Types of Movies

Ghosh, Sudipto; Ahammed, Md. Tabil

doi:10.55529/jmcc22.14.20

Cited by 5 publications

(2 citation statements)

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“…Bhuvaneshwari et al (2022) utilized a Bi-LSTM selfattention CNN model for sentiment analysis of user comments, showing favorable results in sentiment analysis. Ghosh & Ahammed (2022) investigated the influence of sentiment analysis on feedback loops among different types of movies. The findings demonstrate that sentiment analysis can impact the feedback loop between movie genres.…”

Section: A Current Research Status Of Text Sentiment Analysis In the ...mentioning

confidence: 99%

Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things

Mao,

Qian,

Lucas

2023

IEEE Access

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This work aims to introduce Long Short-Term Memory (LSTM) under the Internet of Things (IoT) context to enhance the accuracy and granularity of sentiment analysis in animated online education texts. It employs a multimodal data collection approach and uses IoT technology to gather multimodal textual data from students engaged in animated online education. The data includes students' feedback texts, emotional texts, written texts, and verbal expressions during animated online education. Subsequently, a model named Information Block Bidirectional Long-Short term Memory (IB-BiLSTM) is designed and utilized to construct a sentiment classification model for animated online education texts. Experimental results demonstrate that the model achieves an accuracy of 93.92% and an F1-score of 90.34% for sentiment classification in animated online education texts and the loss function converges to around 0.14. This model effectively captures the emotional changes and evolution during students' learning process. Thus, the proposed model holds significant potential and practical significance for enhancing animated online education's personalization and emotional engagement. It provides valuable insights and guidance for the intelligent development of the education field.

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Section: A Current Research Status Of Text Sentiment Analysis In the ...mentioning

confidence: 99%

Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things

Mao,

Qian,

Lucas

2023

IEEE Access

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“…How to extract valuable information from the data has become one of the hotspots of research, which has attracted increasing attention from both industry and academia. Among them, movie review analysis, as an important task in the realm of natural language processing, seeks to gather sentiment data from textual reviews of films from viewers and it can be used in a variety of contexts, including marketing, product improvement, and public opinion monitoring [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Movie sentiment analysis based on Long Short-Term Memory Network

Li,

Qin,

Zhou

2024

ACE

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An important task in the study of Natural Language Processing (NLP) is the analysis of movie reviews. It finishes the task of classifying movie review texts into sentiment, such as positive, negative or neutral sentiment. Previous works mainly follow the pipeline of LSTM (Long Short-Term Memory Network). The network model is a variant of Recurrent Neural Network (RNN) and particularly suitable for processing natural language texts. Though existing LSTM-based works have improved the performance significantly, we argue that most of them deal with the problem of analyzing the sentiment of movie reviews while ignore analyze the model performance in different application scenarios, such as different lengths of the reviews and the frequency of sentiment adverbs in the reviews. To alleviate the above issue, in this paper, we constructed a simple LSTM model containing an embedding layer, a batch normalization layer, a dropout layer, a one-dimensional convolutional layer, a maximal pooling layer, a bi-directional LSTM layer and a fully connected layer. We used the existing IMDB movie review dataset to train the model, and selected two research scenarios of movie review length and frequency of occurrence of sentiment adverbs to test the model, respectively. From the experimental results, we proposed a model for the scenarios in which the LSTM model handles the problem of sentiment analysis with respect to the dataset construction, model stability and generalization ability, text fragment processing, data preprocessing and feature extraction, model optimization and improvement.

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