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1. www.researchgate.net › publication › 378691218(PDF) Sentiment Analysis of IMDb Movie Reviews Using ...

   www.researchgate.net › publication › 378691218

   Mar 3, 2024 · IMDb movie reviews dataset is preprocessed, cleaned, and tokenized, followed by feature extraction using Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods.

2. www.sciencedirect.com › science › articleMovie recommendation and sentiment analysis using machine ...

   www.sciencedirect.com › science › article

   Jun 1, 2022 · Fig. 1. Flowchart of proposed method. Three datasets have been used for study. 2 of them are for Movie Recommendation and 1 is for Sentiment Analysis. The ones used for recommendation are ‘tmdb_5000_movies.csv’, ‘tbmd_5000_credits.csv’ and the one used for sentiment analysis is ‘reviews.txt’.

3. www.irjmets.com › final › fin_irjmets1655453248SENTIMENT ANALYSIS OF MOVIE REVIEWS USING SUPERVISED MACHINE ...

   www.irjmets.com › final › fin_irjmets1655453248

   Our important objective is to classify movie reviews into positive or negative polarity by using supervised machine learning. Now a days the experience or opinion passed by others is used by human beings to make their decisions. The objective of this paper is to recommend the best movie based on polarity of reviews. In

4. www.researchgate.net › publication › 381884599_AA systematic review of movie recommender systems - ResearchGate

   www.researchgate.net › publication › 381884599_A

   Jul 2, 2024 · With movie releases, apps like Netflix, Viu, Amazon Prime Video, Disney+, etc. have emerged. Thus, Movie Recommender Systems (MRS) are essential to simplify and improve user experience. This ...

5. link.springer.com › chapter › 10Classifying Sentiments from Movie Reviews Using Deep Neural ...

   link.springer.com › chapter › 10

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   Oct 21, 2022 · Then we convert the movie reviews into numeric values. Finally, we classify the movie sentiment using deep neural networks with word embedding and machine learning algorithms with Term Frequency-Inverse document frequency (TF-IDF). The steps of classifying movie review sentiment is as depicted in Fig. 1. Fig. 1.

6. www.semanticscholar.org › paper › Sentiment-Analysis[PDF] Sentiment Analysis of Movie Review Using Supervised ...

   www.semanticscholar.org › paper › Sentiment-Analysis

   The main focus is to determine the polarity of reviews using nouns, verbs, and adjectives as opinion words to improve sentiment analysis of movie reviews using feature-based opinion mining and supervised machine learning. Sentiment Analysis is a new subject in Research and is useful in many other fields. In Modern World, A huge amount of textual data is collected using surveys, comments, and ...

7. People also ask

   How IMDb is used to predict movie reviews?

   • Now a movie name is taken as an input and sent to the movie recommendation model to predict similar movies. Through web scraping from the IMDB site, the reviews of that movie are obtained and sent to the Sentiment Analysis model for classifying the reviews as positive or negative. Fig. 1. Flowchart of proposed method.

   Movie recommendation and sentiment analysis using machine learning

   www.sciencedirect.com/science/article/pii/S2666285X22000176

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   Can a fusion score improve a movie recommendation?

   • In this paper, a hybrid recommendation system is proposed that uses sentiment analysis of user tweets for movies to obtain a sentiment score to improve the recommendation made to the users using a weighted fusion score method .

   Movie recommendation and sentiment analysis using machine learning

   www.sciencedirect.com/science/article/pii/S2666285X22000176

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   What is IMDb movie reviews dataset?

   • IMDb movie reviews dataset is preprocessed, cleaned, and tokenized, followed by feature extraction using Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods.

   Sentiment Analysis of IMDb Movie Reviews Using Traditional Machine

   www.researchgate.net/publication/378691218_Sentiment_Analysis_of_IMDb_Movie_Reviews_Using_Traditional_Machine_Learning_Techniques_and_Transformers

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   How to classify movie review sentiment?

   • The steps of classifying movie review sentiment. As raw review texts also include some HTML tags, brackets, and punctuation. Before building the models, the text is first pre-processed. For noise removal, we need to clean the movie reviews. It reduces the computational complexity in movie sentiment classification based reviews.

   Classifying Sentiments from Movie Reviews Using Deep Neural ... - Sprin…

   link.springer.com/chapter/10.1007/978-3-031-19958-5_37

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   What is 1D convolution in movie sentiment classification?

   • Convolutional Layer: In movie sentiment classification, 1D convolution has been performed along with activation function and max-pooling layer. 1D Convolution: The kernels slide through a list of word embeddings for processing a full sequence of words. As the kernel moves in just one dimension, therefore, it is known as a 1D convolution.

   Classifying Sentiments from Movie Reviews Using Deep Neural ... - Sprin…

   link.springer.com/chapter/10.1007/978-3-031-19958-5_37

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   Does cosine similarity make a good movie recommendation system?

   • This paper describes an approach to a movie recommendation system using Cosine Similarity to recommend similar movies based on the one chosen by the user. Although the existing recommendation systems get the job done, it does not justify if the movie is worth spending time on.

   Movie recommendation and sentiment analysis using machine learning

   www.sciencedirect.com/science/article/pii/S2666285X22000176

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8. www.researchgate.net › profile › Pimwadee-ChaovalitMovie Review Mining: a Comparison between Supervised and ...

   www.researchgate.net › profile › Pimwadee-Chaovalit

   semantics in a particular review could contradict with overall semantic direction (good or bad) of that review. For example, an “unpredictable” camera gives negative