Sentiment Analysis of movie reviews using Microsoft Text Analytics and Google Cloud Natural Language API

Authors

  • S Jaswanth Sai Chowdary  Department of CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • Sk Waseem Farooq  Department of CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • Sk Abubakar Siddique  Department of CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • S Dinesh  Department of CSE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT195130

Keywords:

Sentiment Analysis, TMDB database, Microsoft cognitive services, Google cloud natural language API, classification

Abstract

Sentiment analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. In this paper we focus our task of sentiment analysis on The Movie DB (TMDB) database which is an online database and contains information of movies, television shows and video games including cast, crew and reviews. We examine the sentiment expression to classify the polarity of the movie review on a scale of 1(highly disliked) to 5(highly liked) and perform feature extraction and ranking and use these features to train our multilabel classifier to classify the movie review into its correct label.We will collect the reviews for various movies and perform classification on them. The classification for the reviews are done based on score obtained for the text. We are using two approaches to generate scores for the reviews. These approaches are Microsoft cognitive services and Google cloud natural language API.Finally we are able to generate the scores and can classify them based on movie reviews.

References

  1. Sahu, T. P., & Ahuja, S. (2016). Sentiment analysis of movie reviews: A study on feature selection & classification algorithms. 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).DOI:10.1109/microcom.2016.7522583
  2. Magdum, S. S., &Megha, J. V. (2017). Mining online reviews and tweets for predicting sales performance and success of movies. 2017 International Conference on Intelligent Computing and Control Systems(ICICCS).DOI:10.1109/iccons.2017.8250738
  3. https://developers.themoviedb.org/3/movies/get-movie-reviews
  4. https://azure.microsoft.com/en-in/services/cognitive-services/text-analytics/
  5. https://cloud.google.com/natural-language/
  6. Topal, K., & Ozsoyoglu, G. (2016). Movie review analysis: Emotion analysis of IMDb movie reviews. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).DOI:10.1109/asonam.2016.7752387 

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-12-30

Issue

Volume 5, Issue 1, January-February-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S Jaswanth Sai Chowdary, Sk Waseem Farooq, Sk Abubakar Siddique, S Dinesh, " Sentiment Analysis of movie reviews using Microsoft Text Analytics and Google Cloud Natural Language API, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.147-150, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT195130