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Classification of user’s review using modified logistic regression technique

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Abstract

In recent years, classification and analysis of user reviews or opinions are becoming one of the significant aspects of sentiment analysis. It involves finding the polarity of each review created by the user on social networking through opinion mining. The three review polarity indicators are positive, negative and neutral. User’s sentiments are expressed in specific emotions, numbers, ratings and words for classification. Existing research work lacks accurate results due to the high ambiguity of review classification and analysis in interpreting the overall polarity, thereby proposing a modified logistic regression technique to solve such problems used for sentiment analysis and text processing. The proposed technique involves support count estimation and classification of reviews. It considers multiple independent words having similar meanings in parallel. The movie review dataset is regarded as a reliable source. The performance parameters in the proposed technique outperform the conventional methods by 90%, 78.6%, 75.6% and 76.5% concerning classification accuracy, precision, recall, and f-measure, respectively.

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References

  • Alrumaih A et al (2020) Sentiment analysis of comments in social media. Int J Electric Comput Eng 10:5917–5922

    Google Scholar 

  • Banik N, Hasan Hafizur Rahman M (2018) Evaluation of Naïve Bayes and support vector machines on Bangla textual movie reviews. In: International conference on Bangla speech and language processing (ICBSLP), IEEE, pp 1–6

  • Barnaghi P, et al (2016) Opinion mining and sentiment polarity on twitter and correlation between events and sentiment. In: Second international conference on big data computing service and applications, IEEE, pp 52–57

  • Bhalla R et al (2020) Opinion mining framework using proposed RB-Bayes model for text classification. Int J Electric Comput Eng 9:477–485

    Google Scholar 

  • Dataset: https://www.kaggle.com/nltkdata/movie-review.

  • Fauzi MA (2019) Word2Vec model for sentiment analysis of product reviews in Indonesian language. Int J Electric Comput Eng 9:525–530

    Google Scholar 

  • Fry C, Manna S (2016) Can we group similar amazon reviews: a case study with different clustering algorithms. In: Tenth international conference on semantic computing, IEEE, pp 374–377

  • Hashemzahde B et al (2020) Improving keyword extraction in multilingual texts. Int J Electric Comput Eng 10:5909–5916

    Google Scholar 

  • Hussein DMEDM (2018) A survey on sentiment analysis challenges. J King Saud Univ Eng Sci 30:330–338

    Google Scholar 

  • Iqbal F et al (2019) A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access 7:14637–14652

    Article  Google Scholar 

  • Kumar KLS, et al (2016) Opinion mining and sentiment analysis on online customer review. In: International conference on computational intelligence and computing research (ICCIC), IEEE, pp 1–4

  • Kuo JY et al (2021) Building Student Course Performance Prediction Model Based on Deep Learning. J Inf Sci Eng 37:243–257

    Google Scholar 

  • Medhat W et al (2018) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5:1093–1113

    Article  Google Scholar 

  • Meqdad MN et al (2020) New prediction method for data spreading in social networks based on machine learning algorithm. TELKOMNIKA 18:3331–3338

    Article  Google Scholar 

  • Nafees M, et al (2018) Sentiment analysis of polarity in product reviews in social media. In: 14th international conference on emerging technologies (ICET), IEEE, pp 1–6

  • Ngoc TNT et al (2019) Mining aspects of customer’s review on the social network. J Big Data 6:6–22

    Google Scholar 

  • Parthasarathy G, Tomar DC (2015) Trends in citation analysis. Adv Intell Syst Comput 308:813–821

    Article  Google Scholar 

  • Purba D et al (2020) Classification of instagram fake users using supervised machine learning algorithms. Int J Electric Comput Eng 10:2763–2772

    Google Scholar 

  • Rajeshwari D, Vagdevi S (2020) Framework for opinion as a service on review data of customer using semantics based analytics. Int J Electric Comput Eng 10:5453–5461

    Google Scholar 

  • Rameshbhai CJ, Paulose J (2019) Opinion mining on newspaper headlines using SVM and NLP. Int J Electric Comput Eng 9:2152–2163

    Google Scholar 

  • Reddy R, Shyam GK (2018) Analysis through machine learning techniques: a survey. In: Second international conference on green computing and internet of things (ICGCIoT), IEEE, pp 542–546

  • Reddy R, Shyam GK (2019) Market data analysis by using support vector machine learning technique. Lect Notes Data Eng Commun Technol 28:19–27

    Article  Google Scholar 

  • Sharma R et al (2019) Data mining classification techniques–comparison for better accuracy in prediction of cardiovascular disease. Int J Data Anal Techniq Strat 11:356–373

    Article  Google Scholar 

  • Sihwi SW, et al (2018) Twitter sentiment analysis of movie reviews using information gain and naïve Bayes classifier. In: International seminar on application for technology of information and communication (iSemantic), IEEE, pp 190–195

  • Wang W et al (2017) The impact of sentiment orientations on successful crowdfunding campaigns through text analytics. J IET Softw 11:229–238

    Article  Google Scholar 

  • Wang R et al (2019) A survey on opinion mining: from stance to product aspect. IEEE Access 7:41101–41124

    Article  Google Scholar 

  • Zhao G et al (2019) Exploring users’ internal influence from reviews for social recommendation. IEEE Trans Multimedia 21:771–781

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Kattigenhalli, Yelahanka, Bengaluru, 560064, India

    Raghavendra Reddy & U. M. Ashwin Kumar

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  1. Raghavendra Reddy
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  2. U. M. Ashwin Kumar
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Correspondence to Raghavendra Reddy.

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Reddy, R., Kumar, U.M.A. Classification of user’s review using modified logistic regression technique. Int J Syst Assur Eng Manag 15, 279–286 (2024). https://doi.org/10.1007/s13198-022-01711-4

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  • DOI: https://doi.org/10.1007/s13198-022-01711-4

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