Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

2nd International Conference on Measurement Instrumentation and Electronics

物理学;无线电电子学

Karsi, Redouane^1 ; Zaim, Mounia^1 ; El Alami, Jamila^1

Laboratory of System Analysis, Information Processing and Integrated Management, Mohammadia School of Engineers, Mohammed v University, Rabat, Morocco^1

关键词: Different domains;    E-commerce sites;    Evaluation study;    K nearest neighbor (KNN);    Machine learning techniques;    Sentiment classification;    Standard deviation;    Supervised machine learning;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/870/1/012005/pdf
DOI  :  10.1088/1742-6596/870/1/012005

来源: IOP

【 摘 要 】

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called "sentiment analysis" is born to address the problem of automatically determining the polarity (Positive, negative, neutral,...) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.