Advertisement

Sentiment Analysis and Prediction Using Neural Networks

  • Sneh PaliwalEmail author
  • Sunil Kumar Khatri
  • Mayank Sharma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

Sentiment Analysis or opinion mining have taken many leaps and turns from its starting in early 2000s till now. Advancement in technology, mobile & internet services and ease of access to these services have resulted in more and more engagement of people on the social media platforms for expressing their views and collaborate with people who share similar thoughts. This has led to generation of a large amount of data on the internet and subsequently the need of analysing this data. The sentiment analysis helps different organisations to know how people look to their products and services and what changes are required to improve them. The paper performs sentiment analysis i.e. classification of tweets into positive, negative and neutral on views of a particular product using an inbuilt python library called TextBlob for three platforms i.e. twitter, Facebook and news websites and further it talks about how Artificial Neural Networks (ANN) offer a platform to perform sentiment analysis in a much easier and less time-consuming manner. In this paper Feed-Forward Back propagation neural networks are used to split the data into train and test data and a min-max approach was applied to the data to scale the data and analyse the prediction accuracy of a sentiment using ANN. Precision, recall and accuracy have been calculated to provide a quantitative approach to the results and measure the performance of ANN. We found that such type of neural network is very efficient in predicting the result with a high accuracy.

Keywords

Sentiment analysis Artificial Neural Networks (ANN) Machine learning Feed-forward networks Activation function 

Notes

Acknowledgments

We thank open data platforms like twitter, Facebook and different news sites, which provide data to students like us to perform several types of studies and infer results from them. Also, we would like to thank free software providers like Anaconda platform, which is used in this study, to help people across the data science community to code, develop and analyse different problems.

References

  1. 1.
    Yazhini Priyanka, D., Senthilkumar, R.: Sampling techniques for streaming dataset using sentiment analysis. In: Fifth International Conference on Recent Trends in Information Technology(ICRTIT), pp: 1–6 (2016)Google Scholar
  2. 2.
    Trupthi, M., Pabboju, S., Narasimha, G.: Sentiment analysis on twitter using streaming API. In: 2017 IEEE 7th International Advance Computing Conference, pp. 915–919 (2017)Google Scholar
  3. 3.
    Ray, P., Chakrabarti, A.: Twitter sentiment analysis for product review using lexicon method. In: 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 211–216 (2017)Google Scholar
  4. 4.
    Fang, B., Liang, S., Zou, Q., Huang, W.: Research on sentiment analysis of financial texts based on web. In: 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 248–252 (2017)Google Scholar
  5. 5.
    Soni, A.K.: Multi-lingual sentiment analysis of twitter data by using classification algorithms. In: Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–5 (2017)Google Scholar
  6. 6.
    Bhuiyan, H., Ara, J., Bardhan, R., Rashedul Islam, Md.: Retrieving youtube video by sentiment analysis on user comment. In: IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2017), pp. 474–478. Malaysia (2017)Google Scholar
  7. 7.
    Likhar, M., Kasar, S.L.: Sentiment analysis using sentence minimization with natural language generation (NLG). In: 1st International Conference on Intelligent Systems and Information Management (ICISIM), pp. 134–140 (2017)Google Scholar
  8. 8.
    Ul Hassan, A., Hussain, J., Hussain, M., Sadiq, M., Lee, S.: Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 138–140 (2017)Google Scholar
  9. 9.
    Prasad, A.G., Sanjana, S., Bhat, S.M., Harish, B.S.: Sentiment analysis for sarcasm detection on streaming short text data. In: 2nd International Conference on Knowledge Engineering and Applications, pp. 1–5 (2017)Google Scholar
  10. 10.
    Singla, Z., Randhawa, S., Jain, S.: Statistical and sentiment analysis of consumer product reviews. In: 8th ICCCNT, pp. 1–6 (2017)Google Scholar
  11. 11.
    Juneja, P., Ojha, U.: Casting online votes: to predict offline results using sentiment analysis by machine learning classifiers. In: 8th ICCCNT, pp. 1–6 (2017)Google Scholar
  12. 12.
    Sindhu, C., Vyas, D.V., Pradyoth, K.: Sentiment analysis based product rating using textual reviews. In: International Conference on Electronics, Communication and Aerospace Technology ICECA, vol. 2, pp. 727–731 (2017)Google Scholar
  13. 13.
    Wang, H., Wang, N., Yeung, D.-Y.: Collaborative deep learning for recommender systems, KDD ’15, pp. 1235–1244 (2015)Google Scholar
  14. 14.
    Borele, P., Borikar, D.A.: An approach to sentiment analysis using artificial neural network with comparative analysis of different techniques. IOSR J. Comput. Eng. (IOSR-JCE) 18(2). e-ISSN: 2278-0661, p-ISSN: 2278-8727Google Scholar
  15. 15.
  16. 16.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sneh Paliwal
    • 1
    Email author
  • Sunil Kumar Khatri
    • 1
  • Mayank Sharma
    • 1
  1. 1.Amity Institute of Information Technology, Amity UniversityNoidaIndia

Personalised recommendations