Sentiment Analysis for Product Recommendation Using Random Forest

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Analysis of sentiments is to analyze the natural language and to find the emotions, express by the human beings. The idea behind sentiment analysis is to determine polarity of textual opinion given by person. Sentiment Analysis is useful in product recommendations. Based on the reviews given by the user; the products can be recommended to another user. Major product websites are using sentiment analysis to understand the popularity and problems with the product. Sentiment analysis mainly formulated as two class classification problem, positive and negative. Sentiment analysis using ordinal classification gives more clear idea about sentiments. The proposed system determines polarity of reviews given by users, using ordinal classification. The system will give polarity using machine learning algorithms SVM and Random Forest. The achieved polarity will be used to provide recommendation to users.

     


  • Keywords


    Natural Language Processing; Ordinal classification; product recommendation; Random Forest; Sentiment Analysis

  • References


      [1] Bhavitha, B. K., Anisha P. Rodrigues, and Niranjan N. Chiplunkar. "Comparative study of machine learning techniques in sentimental analysis." Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on. IEEE, 2017.

      [2] Hegde, Yashaswini, and S. K. Padma. "Sentiment Analysis Using Random Forest Ensemble for Mobile Product Reviews in Kannada." Advance Computing Conference (IACC), 2017 IEEE 7th International. IEEE, 2017.

      [3] Wan, Yun, and Qigang Gao. "An ensemble sentiment classification system of twitter data for airline services analysis." Data Mining Workshop (ICDMW), 2015 IEEE International Conference on. IEEE, 2015.

      [4] Rosa, Renata L., Demsteneso Z. Rodriguez, and Graca Bressan. "Music recommendation system based on user's sentiments extracted from social networks." IEEE Transactions on Consumer Electronics 61.3 (2015): 359-367.

      [5] Zheng, Xiaoyao, et al. "A tourism destination recommender system using users’ sentiment and temporal dynamics." Journal of Intelligent Information Systems (2018): 1-22.

      [6] Parmar, Hitesh, Sanjay Bhanderi, and Glory Shah. "Sentiment Mining of Movie Reviews using Random Forest with Tuned Hyperparameters." (2014).

      [7] Kuzey, Cemil, Ali Uyar, and Dursun Delen. "An Investigation of the Factors Influencing Cost System Functionality Using Decision Trees, Support Vector Machines and Logistic Regression." (2018).

      [8] Rosenthal, Sara, Noura Farra, and Preslav Nakov. "SemEval-2017 task 4: Sentiment analysis in Twitter." Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 2017.

      [9] Kethavath, Shivaraju. Classification of Sentiment Analysis on Tweets using Machine Learning Techniques. Diss. 2015.

      [10] Soni, Rishabh, and K. James Mathai. "Improved Twitter Sentiment Prediction through Cluster-then-Predict Model." arXiv preprint arXiv:1509.02437 (2015).


 

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Article ID: 14492
 
DOI: 10.14419/ijet.v7i3.3.14492




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