IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications

Abstract

The proliferation of Web 2.0 has altered the general styles of research and academia. The Internet of things (IoT) is one of the core IT fields in the current arena, as it transforms the physical objects into sensor-based virtual intelligent objects. There exist millions of physical objects that are interlinked with sensor-based devices in order to mimic the real-world intelligence. The IoT industry is taking benefit of big data sentiment classification in reshaping and improving the sensor-based integrated information processing systems. Sentiment analysis (SA) proved to be the backbone in orientation and betterment of IoT technology. Opinion mining is the problem of natural language processing (NLP) and field of data mining and machine learning. The goal of opinion mining is the identification of public sentiments, feeling and appraisals. Opinion mining is the significant research area in this new arena of web mining and text classification. It draws great attention in the last few years due to social media sites and their usage. Proliferation of internet applications has changed the public communication trends. Online users adopt social media sites for communication and collaboration. Most valuable communication channels are Twitter, Facebook, Tumbler and Plurk. There exist various classes of webbased application for communication, i.e. discussion forum, blogs, review sites and

Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

“…Semantic Orientation of user generated contents has great importance not only to observer and analysts of numerous organization but socio monitoring can benefit in reshaping the Decision Making Process [8], [9], [10]. Natural Language Engineering proved to be the key source in the process of sentiment analysis and text normalization for formal as well as informal opinion bearing text [11], [12]. Beside the existence of formal and standard opinionative contents, the contemporary user collaboration contains colloquial as well as non-standard multilingual content, which poses a number of challenges in mining feelings and moods [13].…”