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Sentiment Analysis of Smartphone Product Reviews Using Weightage Calculation

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Advances in Computing and Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Sentiment analysis is extremely useful in monitoring the social media and the most popular tool for text classification, and analyzes the input and informs you if it is positive, negative, or neutral. It helps to collect large amount of data systematically and it extracts the subjective information from them. Humans have the indelible ability to determine sentiment which is time-consuming process, conflicting, and costly in a business context. It is not practical to have people individually read all the reviews of the customer and scores them for sentiment. So, to overcome this, sentimental analysis models has been developed. In our proposed system, we are using weightage classification model to analyze the tweets from Twitter API and classify based on their respective sentiment.

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References

  1. Trupthi, M., Pabboju, S., & Narasimha, G. (2017). Sentiment Analysis on Twitter using Streaming API. In IEEE 7th International Advanced Computing Conference.

    Google Scholar 

  2. Kumari, U., Sharma, A. K., & Soni, D. (2017). Sentimental analysis of smartphone productreview using svm classification technique. In International Conference on Energy Communication, Data Analytics and Soft Computing.

    Google Scholar 

  3. Giatogolan, M. et al. (2016). Sentimental analysis leveraging emotions and word embeddings. Expert Systems with Applications International Journal.

    Google Scholar 

  4. Sharma, P., & Moh, T. S. (2016). Peridiction of india election using sentiment analysis on hindhi twitter. In International Conference on Bigdata.

    Google Scholar 

  5. Mishra, P., Rajnish, R., & Kumar, R. (2016). Sentiment analysis of twitter data : case study on digital India. In International Conference on Information Technology.

    Google Scholar 

  6. Hussein, D.-M. E. D. M. (2016). A survey on sentiment analysis challenges. Journal of King Saud University–Engineering Sciences.

    Google Scholar 

  7. Shukri, S. E. & Yaghi, R. I. (2015). Twitter sentiment analysis: case study in automotive industry. In IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies.

    Google Scholar 

  8. Bhuvan, M. S., Rao, V. D., Jain, S., Ashwin, T. S., & Guddeti, R. M. R. (2015). Semantic sentiment analysis using context specific grammar. In International Conference on Computing, Communication and Automation.

    Google Scholar 

  9. Bar, K. (2013). Sentiment analysis of movie reviews and twitter statuses. Israel: Tel Aviv U.

    Google Scholar 

  10. Liang, P. W., & Dai, B. R. (2013, June 3–6). Opinion mining on social media data. In IEEE 14th International Conference on Mobile Data Management, Milan, Italy.

    Google Scholar 

  11. Nguyen, V. D., & Varghese, B. (2013). Royal birth of 2013: analysing and visualising public sentiment in the UK using twitter. In Research Gate.

    Google Scholar 

  12. Mittal, N., Agarwal, B., Chouhan, G., Bania, N., & Pareek, P. (2013). Sentiment analysis of hindi review based on negation and discourse relation. In Proceedings of International Joint Conference on Natural Language Processing.

    Google Scholar 

  13. Mukherjee, S., & Malu, A. (2012). TwiSent: a multistage system for analyzing sentiment in twitter. In Proceedings of the 21st ACM international Conference on Information and Knowledge Management.

    Google Scholar 

  14. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh Conference on International Language Resources and Evaluation.

    Google Scholar 

  15. Gune, H., Bapat, M., Khapra, M. M., & Bhattacharyya, P. (2010). Verbs are where all the action lies: experiences of shallow parsing of a morphologically rich language. In Proceedings of the 23rd International Conference on Computational Linguistics, pp. 347–355.

    Google Scholar 

  16. Yessenov, K., & Misailovic, S. (2009). Sentiment analysis of movie review comments. Methodology.

    Google Scholar 

  17. Munjal, P., Kumar, L., Kumar, S., & Banati, H. (2019). Evidence of ostwaid ripening in opinion driven dynamics of mutually competitive social networks. Physica A, 522, 182–194.

    Article  Google Scholar 

  18. Munjal, P., Kumar, S., Kumar, L., & Banati, A. (2017). Opinion dynamics through natural phenomenon of grain growth and population migration. In Hybrid Intelligence for Social Networks (pp. 161–175). Springer: Cham.

    Google Scholar 

  19. Munjal, P., Narula, M., Kumar, S., & Banati, H. (2018). Twitter sentiments based suggestive framework to predict trends. Journal of Statistics and Management Systems, 21(4), 685–693.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Science and Engineering, Alvas Institute of Engineering and Technology, Moodbidri, 574225, Karnataka, India

    Jayantkumar A. Rathod, Shetty Vignesh, Aishwary J. Shetty,  Pooja &  Nikshitha

Authors
  1. Jayantkumar A. Rathod
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  2. Shetty Vignesh
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  3. Aishwary J. Shetty
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  4. Pooja
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  5. Nikshitha
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Corresponding author

Correspondence to Jayantkumar A. Rathod .

Editor information

Editors and Affiliations

  1. Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. University of Southern Denmark, Odense, Denmark

    Kannan Govindan

  3. Amity University, Jaipur, Rajasthan, India

    Ramesh C. Poonia

  4. Amity University, Jaipur, Rajasthan, India

    Sandeep Kumar

  5. University of Bahrain, Zallaq, Bahrain

    Wael M. El-Medany

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Rathod, J.A., Vignesh, S., Shetty, A.J., Pooja, Nikshitha (2020). Sentiment Analysis of Smartphone Product Reviews Using Weightage Calculation. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_40

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