Socio monitoring framework (SMF): Efficient sentiment analysis through informal and native terms

Abstract

Prediction and analysis of public expression is the trending topic of the current research arena. Opinion mining (a.k.a. Sentiment Analysis) is the automated orientation of public sentiments, views, suggestions, and opinions. It assists in estimating the popularity of products, events, services, and even political policies via user-generated content. Machine learning based supervised, semi-supervised, and unsupervised lexicon oriented techniques are applicable in the semantic orientation of public opinions about numerous real world entities. It is observed that socio channels contain real-time contents, which sometimes face the intricacy of informality, Slangs, Vernacular (Native terms), and sarcasm; however, these indicators provide high visibility of sentiments and opinions in terms of orientation. Unfortunately, the unclear perceptiveness of such contents lack in optimized orientation, and supervised machine learning systems are inappropriate where the Lexicon based opinion mining methods are preferred over learning based ones when training data is not adequate. In this paper, we seek to improve the performance of lexicon-based sentiment analysis by incorporating novel linguistic features such as vernaculars, slangs, and sarcasm for monitoring the social media contents up to a more realistic level. The core contributions are sarcasm detection and identification of vernacular terms. The performance of the proposed unsupervised lexicon-based framework over native, informal, and sarcastic opinion bearing terms is assessed via numerous experiments. For this, we utilized tweets relevant to two key domains, including Product and Politics. Experimental outcomes revealed that the proposed system outperformed the existing supervised and semi-supervised systems as 84.24%, and 82.35% of accuracies are achieved over informal and sarcastic contents for product and politics domains, respectively. The average accuracy for both domains is 83.29%.