Sentiment classification of social media reviews using an ensemble classifier

These days it has become a common practice for business organizations and individuals to make use of social media for sharing the opinions about the products or the services.  Consumers are also ready to share their views on certain products or commodities.  Thus huge amount of unstructured social media data gets generated day by day. Gradually heap of text data will be formed in many areas like automated business, education, health care, and show business and so on. Opinion mining also referred as sentiment analysis or sentiment classification, deals with mining of the review text and classifying the opinions or the sentiments of that text as positive or negative. In this paper we propose an ensemble classifier model consisting of Support Vector Machine and Artificial Neural Network. It combines the knowledge from two feature sets for sentiment classification. The proposed model shows the acceptable performance in terms of accuracy when compared with the baseline model.

[1]  Liu Yongbin,et al.  Opinion Objects Identification and Sentiment Analysis , 2015 .

[2]  Honghui Chen,et al.  A Hierarchical Neural-Network-Based Document Representation Approach for Text Classification , 2018 .

[3]  Tao Chen,et al.  Combining Convolutional Neural Network and Support Vector Machine for Sentiment Classification , 2015, SMP.

[4]  M. Ali Fauzi,et al.  Word2Vec model for sentiment analysis of product reviews in Indonesian language , 2019, International Journal of Electrical and Computer Engineering (IJECE).

[5]  Rashid Ali,et al.  Feature extraction and analysis of online reviews for the recommendation of books using opinion mining technique , 2016 .

[6]  Xin Wang,et al.  Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets , 2010, COLING.

[7]  M. Govindarajan.  Sentiment Analysis of Movie Reviews using Hybrid Method of Naive Bayes and Genetic Algorithm , 2014 .

[8]  Júlio C. Nievola,et al.  Using a Genetic Algorithm Approach to Study the Impact of Imbalanced Corpora in Sentiment Analysis , 2015, FLAIRS.

[9]  Pushpak Bhattacharyya,et al.  A Hybrid Deep Learning Architecture for Sentiment Analysis , 2016, COLING.

[10]  Santanu Kumar Rath,et al.  Classification of sentiment reviews using n-gram machine learning approach , 2016, Expert Syst. Appl..

[11]  J. Fernando Sánchez-Rada,et al.  Enhancing deep learning sentiment analysis with ensemble techniques in social applications , 2020 .

[12]  P. Deepa Shenoy,et al.  Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier , 2016, World Wide Web.

[13]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[14]  Genetic Algorithm based Feature Selection in High Dimensional Text Dataset Classification , 2015 .

[15]  Xiaojun Wan,et al.  CLOpinionMiner: Opinion Target Extraction in a Cross-Language Scenario , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[16]  Joy Paulose,et al.  Opinion mining on newspaper headlines using SVM and NLP , 2019, International Journal of Electrical and Computer Engineering (IJECE).

[17]  Kamal Eddine El Kadiri,et al.  A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document , 2018 .

[18]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[19]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[20]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[21]  Santanu Kumar Rath,et al.  Document-level sentiment classification using hybrid machine learning approach , 2017, Knowledge and Information Systems.

[22]  Yaxin Bi,et al.  Improved lexicon-based sentiment analysis for social media analytics , 2015, Security Informatics.

[23]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[24]  Justin Zhijun Zhan,et al.  Sentiment analysis using product review data , 2015, Journal of Big Data.

[25]  Ali Selamat,et al.  Sentiment analysis using Support Vector Machine , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[26]  Huan Liu,et al.  Feature Selection for Classification: A Review , 2014, Data Classification: Algorithms and Applications.

[27]  Ibrahim Ziedan,et al.  A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet , 2018 .

[28]  Samir Tartir,et al.  Semantic Sentiment Analysis in Arabic Social Media , 2017, J. King Saud Univ. Comput. Inf. Sci..

[29]  Taghi M. Khoshgoftaar,et al.  Big Data: Deep Learning for financial sentiment analysis , 2018, Journal of Big Data.

[30]  R. M. Chandrasekaran,et al.  A comparative performance evaluation of neural network based approach for sentiment classification of online reviews , 2016, J. King Saud Univ. Comput. Inf. Sci..