This paper presents an efficient approach for sentiment and domain analysis of text sentences using POS tagging and random forest classifier machine learning approach. POS tagging technique is used for sentiment analysis while Random forest classifier is used for domain analysis of the text sentences. Various categories of sentiments are defined as positive, neutral, and negative while the domain’s categories are defined on various real & professional life sentences to train the system like education & research, personal, marketing & advertizement, security of nation, political, religious, sports and legal issues. Every text sentence always reflects the domain’s categories along with its sentiments. Therefore, Analyzing domain of text sentences along with sentiments is a challenging task and can be useful for various applications based on human computer interaction. The experimental result shows that the proposed method works effectively, efficiently and can be applied on real life applications where obligatory actions are taken automatically through sentences.

Random forest classifier, Machine Learning, Domain Analysis,Sentiment Analysis, Human computer interaction

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