Analysis of Sentiments in Movie Reviews using Supervised Machine Learning Technique

Regina, I.Anette; Sengottuvelan, P.

doi:10.1109/iccct53315.2021.9711848

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…I.Anette Regina. [5] proposed method contains a random collection of 20,000 analyses from the Amazon dataset and performed Segmentation, Segmentation, Noise Removal, Stemming and Vectorization of reviews. [6] devised and compared various techniques like Bag of words models, n-grams for using semantic information to improve the performance of sentiment analysis and analysed the Movie reviews using various techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis on Movie Reviews

2024

IRJMETS

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Opinion mining, which is another name for sentiment analysis, is a sophisticated method of extracting the emotional undertones from written content. This analytical method goes well beyond just classifying feelings as positive or negative; rather, it explores the complex range of emotions, attitudes, and viewpoints that are expressed through language. Its importance cuts across all areas and has a significant influence on strategic planning initiatives and decision-making procedures.Moreover, sentiment analysis transforms the way viewers browse movie reviews in the entertainment sector. Users can quickly get a collective perspective by aggregating sentiments from several platforms, which helps them make well-informed decisions while choosing movies. While negative reviews provide insightful information about potential inconsistencies with personal tastes, positive evaluations are like beacons for delightful movie encounters. Thus, sentiment analysis not only improves entertainment consumption but also supports personalized viewing experiences, resulting in higher consumer happiness.This study essentially highlights the revolutionary potential of sentiment analysis in a variety of industries, stressing its critical role in developing flexible strategies and improving the experiences of both customers and employees.

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Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis on Movie Reviews

2024

IRJMETS

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“…Machine learning algorithms and DL models are two NLP methods used for text review classification [7]. Traditional ML algorithms have been widely utilized to perform sentiment classification in various domains [8][9][10], obtaining greater accuracy than lexicon-based methods [11]. However, traditional ML algorithms struggle with complex text reviews and long text sequences, which can lead to less accurate results [12,13].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Deep Learning for Thai Sentiment Analysis

Khamphakdee

Seresangtakul

2023

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The number of reviews from customers on travel websites and platforms is quickly increasing. They provide people with the ability to write reviews about their experience with respect to service quality, location, room, and cleanliness, thereby helping others before booking hotels. Many people fail to consider hotel bookings because the numerous reviews take a long time to read, and many are in a non-native language. Thus, hotel businesses need an efficient process to analyze and categorize the polarity of reviews as positive, negative, or neutral. In particular, low-resource languages such as Thai have greater limitations in terms of resources to classify sentiment polarity. In this paper, a sentiment analysis method is proposed for Thai sentiment classification in the hotel domain. Firstly, the Word2Vec technique (the continuous bag-of-words (CBOW) and skip-gram approaches) was applied to create word embeddings of different vector dimensions. Secondly, each word embedding model was combined with deep learning (DL) models to observe the impact of each word vector dimension result. We compared the performance of nine DL models (CNN, LSTM, Bi-LSTM, GRU, Bi-GRU, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU) with different numbers of layers to evaluate their performance in polarity classification. The dataset was classified using the FastText and BERT pre-trained models to carry out the sentiment polarity classification. Finally, our experimental results show that the WangchanBERTa model slightly improved the accuracy, producing a value of 0.9225, and the skip-gram and CNN model combination outperformed other DL models, reaching an accuracy of 0.9170. From the experiments, we found that the word vector dimensions, hyperparameter values, and the number of layers of the DL models affected the performance of sentiment classification. Our research provides guidance for setting suitable hyperparameter values to improve the accuracy of sentiment classification for the Thai language in the hotel domain.

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