Abstract
Sentiment analysis is a method of extracting subjective information from customer reviews. The analysis helps to reveal the consumer insights about the product, a theme, or a service. In the existing literature, various methods such as BoW and TF-IDF are employed for sentiment analysis and deep learning methods are not explored much. We made an attempt to apply Word2Vec feature weighting method for this problem. We carried out various experiments for sentiment analysis on a large dataset IMDB that contains movie review. We compared various feature weighting methods and analyzed using different classifiers, and the best combination was determined. From the experimental results, we conclude that Word2Vec with SGD is the best combination for sentiment classification problem on IMDB dataset. The result shown in the paper can be used as a base for future exploration of opinioned value on any textual data.
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25 May 2019
In the original version of the book, the following post-publication corrections should be incorporated in the chapter "Comparative Evaluation of Various Feature Weighting Methods on Movie Reviews".
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Acknowledgements
We would like to thank Science and Engineering Research Board, Govt. of India, for funding this work (Award Number: ECR/2016/000484). We would also like to thank the management of VIT University, Chennai, for extending their support, where this research work was carried out.
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Sivakumar, S., Rajalakshmi, R. (2019). Comparative Evaluation of Various Feature Weighting Methods on Movie Reviews. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_64
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DOI: https://doi.org/10.1007/978-981-10-8055-5_64
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