Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning

A., Razia Sulthana; Jaithunbi, A. K.; Harikrishnan, Haritha; Vijayakumar, V.

doi:10.4018/ijitwe.311428

Cited by 8 publications

(6 citation statements)

References 38 publications

(23 reference statements)

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“…The LDA model is used to extract the theme of movie reviews and identify the emotional tendency related to the theme, which improves the content-based recommendation algorithm and confirms that the diversity of recommendation list has been significantly improved [13]. Razia Sulthana et al proposed a method for classifying comments using a classifier-based Bagging algorithm on a newly constructed SVM classifier, and compared the results of the proposed method with similar existing work, concluding that the method achieves better results compared to existing systems [14]. Prabu…”

Section: Introductionmentioning

confidence: 73%

Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Tong

2024

JCC

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Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users' movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.

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Section: Introductionmentioning

confidence: 73%

Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Tong

2024

JCC

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“…In the study by Razia Sulthana A. et al [5], the authors perform sentiment analysis on movie reviews taken from social networking sites using machine learning algorithms and natural language processing techniques. Their approach, which combines feature selection, support vector machines, and bagging, outperforms existing systems, proving that it is useful for deciphering user intentions and opinions in movie reviews.…”

Section: Related Workmentioning

confidence: 99%

“…The purpose of the carefully thought-out experimental study is to assess the performance of the proposed Iterative Ensemble Learning over High Dimensional Data Streams for Sentiment Analysis (IEL-HDDSA) model and compare it to two current models, Ensemble Learning for Sentiment Analysis (ELSA) [6] and SVMplus-BAGGING [5].…”

Section: Experimental Studymentioning

confidence: 99%

“…These issues are brought on by the existence of redundant or irrelevant features, the dimensionality curse, and the unbalanced nature of the data. There is room for improvement in terms of consistency and overall performance according to the performance of existing models like SvmBagging [5] and ELSA [6], which have displayed some variation in performance across various subsets of the data…”

Section: Introductionmentioning

confidence: 99%

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Iterative Ensemble Learning over High Dimensional Data for Sentiment Analysis

Saileela P,

Rao

2024

SCPE

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For sentiment analysis in particular, the problem of processing and analyzing high-dimensional data becomes more prominent in recent past. This is where the IEL-HDDSA model, which aims to increase accuracy and performance in complex, high-dimensional data streams sentiment analysis comes into play. Iterative approach in ensemble learning; a contribution to the field. It integrates preprocessing techniques such as tokenization, stop word removal, lemmatization and the collection of sentiment-related features. Then the training corpus is divided by label, and features with high mutual information are selected. Highly replicated points of data for model training can also be identified at this point. First a Naive Bayes model is trained, then later it's placed in an ensemble as part of bagging. Its major advantage over earlier methods is that IEL-HDDSA can iteratively train on selected subsets of data until the performance in sentiment analysis for high-dimensional objects reaches an optimum level. A 10-fold cross validation method was used to rigorously evaluate the performance of this model, which showed consistently high levels of operation with almost no variation across different measures. IEL-HDDSA's precision ranged from 0.9359 to 0.9492, and its specificity was between 0. Its accuracy differed from 0.93 to around 0.95, and its F1-measure fluctuated between the values of about 0.94 and above; so here too balance was well maintained in a manner that satisfied both precision and recall requirements equally. The false alarming rate fell from 0.056 to 0.1, a fairly low ratio of incorrect positive classifications; Moreover, MCC quantities ranged from 0.8668 to 0. These results testify to the IEL-HDDSA model's stable effectiveness and high reproducibility in sentiment analysis applications, especially for massive data flows.

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“…The Grid Search algorithm then creates a grid of all possible hyperparameter combinations and tests each combination using cross-validation to evaluate its performance. By systematically testing all possible combinations, Grid Search aims to identify the optimal set of hyperparameters that will result in the best performance for the model (Hamida et al, 2020;Sulthana et al, 2022). The use of cross-validation helps to prevent overfitting by evaluating the performance of the model on data that was not used for training (Lin et al, 2008;Nugraha and Sasongko, 2022).…”

Section: Hyperparameter Of Svmmentioning

confidence: 99%

Spam classification problems using support vector machine and grid search

Dewi,

Indriawan,

Christanto

2023

Int. J. Appl. Sci. Eng.

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Spam classification is an important task in identifying unwanted and potentially harmful emails for internet users. The increasing number of internet users highlights the growing importance of handling spam effectively. In this paper, we propose an approach for spam classification using Support Vector Machines (SVM) with grid search hyperparameter optimization. Our research differs from existing studies by specifically focusing on the integration of SVM with grid search to achieve optimal hyperparameter tuning. Additionally, we provide a unique dataset comprising diverse samples of spam emails for evaluation purposes. We also employ pre-processing techniques, including the removal of unnecessary words such as stop words and punctuation marks, as well as word stemming to convert words into their base forms. To optimize the performance of the SVM model, we use Grid Search to determine the optimal values for hyperparameters, including C, gamma, and the kernel. The results of the first experiment using SVM with the first dataset show that grid search yields the optimal parameters {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}, resulting in an accuracy improvement from 98.02% to 98.47%. In the second experiment using the second dataset, the accuracy obtained is 99.1%, compared to the previous non-optimized parameters which achieved 98.8%. These results indicate a significant improvement in spam classification accuracy. The experimental results demonstrate that our approach outperforms existing methods in terms of accuracy, precision, and recall. The findings of our research have significant implications for improving spam detection systems and enhancing the overall effectiveness of email communication.

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Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning

Cited by 8 publications

References 38 publications

Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Iterative Ensemble Learning over High Dimensional Data for Sentiment Analysis

Spam classification problems using support vector machine and grid search

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