S-Sense: A Sentiment Analysis Framework for Social Media Monitoring Applications

Abstract

Today the amount of social media usage has increased exponentially. Many businesses and organizations including market research agencies are seeking for tools which could perform real-time sentiment analysis on this explosive “big data” contents. In this paper, we propose S-Sense, a framework for analyzing sentiment on Thai social media. The proposed framework consists of analysis modules and language resources. Two main analysis modules, intention
and sentiment, are based on classification algorithm to automatically assign appropriate intention and sentiment class labels for a given text. To train classification models, language resources, i.e., corpus and lexicon, are needed. Corpus consists of a collection of texts manually labeled with appropriate
intention and sentiment classes. Lexicon consists of both general terms from dictionary and clue terms which help identifying the intention and sentiment. To evaluate performance and robustness of the analysis modules, we prepare a data set from Twitter posts and Pantip web board in mobile service domain. The experiments are set up to compare the performance between two different lexicon sets, i.e., general and clue terms. The results show that incorporating clue terms into feature vectors for constructing the classification models yield significant improvement in terms of accuracy. The proposed S-Sense framework could be potentially applied for many applications including social media monitoring for improving market brand and campaign management.