efficient multi domain adaptation in sentiment analysis using machine learning and cross domain semantic library

Abstract

Nowadays, the rapid growth of the internet has led to the way for most effortless data generation. These data can be in the form of web pages, blogs, emails, posts on various social networks, or anything that is uploaded to the internet. There must be a technique to retrieve valuable information from this vast data storage. Classification is one of the retrieval techniques for automatic categorization of the data into specified categories. Sentiment Analysis (SA) is the classification problem that is necessary to scrutinize the user-generated data into any of the two classifications (negative or positive). Sentiment Analysis is implemented by machine learning techniques and lexicon-oriented techniques. Due to accuracy, simplicity, and adaptability, machine-learning approaches have lured the researchers. Traditional sentiment analysis techniques are trained on one topic (also called the domain) and tested on the same topic. newlineThe domain on which the machine is trained is called the source domain, and the testing domain is called the target domain. Sometimes labelled data are not available in target domains. The traditional SA models could not deal with these missing labelled data, and the accuracy of traditional machine learning models degrades largely if they are trained on one domain (called source domain) and classify the data of different domain (called target domain which is different from the source domain and labels are not available). This situation is considered as a domain adaptation. To improve the classification accuracy, the machine needs to be trained on corresponding target domain data, but to label each new domain is a difficult and time-consuming task. Hence, the domain adaptation technique is needed to solve the problem of data labelling and make the machine general enough to classify the data of the domain on which it is not trained. The similarity measure plays a vital role in domain adaptation for selecting important pivot (common) features from the target domain that matches source domains. T