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Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer

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Abstract

Movies have been important in our lives for many years. Movies provide entertainment, inspire, educate, and offer an escape from reality. Movie reviews help us choose better movies, but reading them all can be time-consuming and overwhelming. To make it easier, sentiment analysis can classify movie reviews into positive and negative categories. Opinion mining (OP), called sentiment analysis (SA), uses natural language processing to identify and extract opinions expressed through text. Naive Bayes, a supervised learning algorithm, offers simplicity, efficiency, and strong performance in classification tasks due to its feature independence assumption. This study evaluates the performance of four Naïve Bayes variations using two vectorization techniques, Count Vectorizer and Term Frequency–Inverse Document Frequency (TF–IDF), on two movie review datasets: IMDb Movie Reviews Dataset and Rotten Tomatoes Movie Reviews. Bernoulli Naive Bayes achieved the highest accuracy using Count Vectorizer on the IMDB and Rotten Tomatoes datasets. Multinomial Naive Bayes, on the other hand, achieved better accuracy on the IMDB dataset with TF–IDF. During preprocessing, we implemented different techniques to enhance the quality of our datasets. These included data cleaning, spelling correction, fixing chat words, lemmatization, and removing stop words. Additionally, we fine-tuned our models through hyperparameter tuning to achieve optimal results. Using TF–IDF, we observed a slight performance improvement compared to using the count vectorizer. The experiment highlights the significant role of sentiment analysis in understanding the attitudes and emotions expressed in movie reviews. By predicting the sentiments of each review and calculating the average sentiment of all reviews, it becomes possible to make an accurate prediction about a movie’s overall performance.

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Data Availibility Statement

The data that support the findings of this study are openly available through the Open Science Framework at https://github.com/Ankit152/IMDB-sentiment-analysis.git and https://www.kaggle.com/datasets/talha002/rottentomatoes-400k-review

Abbreviations

ABSA:

Aspect-based sentiment analysis

AI:

Artificial intelligence

BOWs:

Bag-of-words

BNB:

Bernoulli Naive Bayes

CNB:

Complement Naive Bayes

CV:

Cross-validation

DL:

Deep learning

GNB:

Gaussian Naive Bayes

GS:

Grid search

IMDB:

Internet movie database

KNN:

K-Nearest Neighbours

SVM:

Support vector machines

ML:

Machine learning

MNB:

Multinomial Naive Bayes

NB:

Naive Bayes

NLP:

Natural language processing

NLTK:

Natural language tool kit

OP:

Opinion mining

RT:

Rotten Tomatoes

TP:

True Positive

TN:

True Negative

FP:

False Positive

FN:

False Negative

SA:

Sentiment analysis

TF–IDF:

Term Frequency–Inverse Document Frequency

Word2vec:

Word to vector

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Acknowledgements

We sincerely thank everyone who helped us finish this research paper. We are grateful to the participants for their helpful feedback and ideas, which improved our research methods and the quality of our results. We appreciate everyone who gave their time to join our study, as this research wouldn’t have been possible without them. Thank you to everyone who took the time to contribute to this research paper.

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Author notes

  1. Mian Muhammad Danyal, Muzammil Khan, Subhan Ullah, Muhammad Bilal Ghaffar, Wahab Khan have contributed equally to this work.

Authors and Affiliations

  1. Center for Excellence in Information Technology, Institute of Management Sciences, Peshawar, 24720, Pakistan

    Mian Muhammad Danyal

  2. Department of Computer Science, City University of Science and Information Technology, Peshawar, 25000, Pakistan

    Mian Muhammad Danyal, Subhan Ullah, Muhammad Bilal Ghaffar & Wahab Khan

  3. Department of Computer and Software Technology, University of Swat, Swat, 19130, Pakistan

    Sarwar Shah Khan & Muzammil Khan

  4. Department of Computer Science, Iqra University Swat Campus, Swat, 19130, Pakistan

    Sarwar Shah Khan

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Contributions

The author contributions are as follow: “Conceptualization, MMD and SSK; methodology, MBG and MK; software, MMD, SU; validation, SSK and WK; formal analysis, MK, WK, and MBG; investigation, SU; data curation, SU and SSK; writing-original draft preparation, MMD, and MBG; writing-review and editing, SSK; visualization, MBG, and MK.

Corresponding author

Correspondence to Muzammil Khan.

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Danyal, M.M., Khan, S.S., Khan, M. et al. Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer. Soc. Netw. Anal. Min. 14, 87 (2024). https://doi.org/10.1007/s13278-024-01250-9

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