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

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Information and Knowledge in Internet of Things

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The Internet of Things (IoT) integrates physical objects in a networked real-world environment utilizing sensor-based software technologies in order to connect, share and exchange data with other relevant devices or platforms over the Internet. The contemporary business intelligence solutions encourage the IoT industry to reap benefits of big data dependent pervasive semantic orientation of public moods and relevant information shared in textual form using context aware integrated information management, by means of opinion mining through social media-based pervasive hybrid applications. Opinion mining (OM), also referred as pervasive sentiment analysis, is a process of extracting user orientation regarding products, services, businesses and other entities. It aims to classify an opinionative clue into positive or negative based on the sense and semantic category, which remains hidden from the human eye and non-pervasive applications.

The microblog and social media-based information is intrinsically hybrid in nature, which may comprise an ample amount of ubiquitous noise. The public opinions can be classified through supervised, semi-supervised, or unsupervised classifiers. Effective sentiment analysis is common to use in data science research, but unfortunately microblogging services like Twitter allows short text for communication, which compels online publishers to post unstructured and short form of opinions towards the target entities. Unstructured and short form of tweets makes it difficult to extract meaningful and accurate sentiment orientation. This limitation can be tackled through an effective preprocessing of text. Text normalization or preprocessing is the process of removing undesired symbols, tags and conversion of unstructured data into valuable information in order to make quality input for efficient sentiment analysis. It is observed that existing preprocessors and text normalizers ignored the informal nature of text especially slangs and subject tags due to scarcity of linguistic resources, which sometimes affects the sentiment accuracy. Therefore, this research proposes a framework for effective preprocessing of text in order to generate quality input. The proposed framework for an effective text preprocessing involves the following:

  1. (i).

    Extraction of text

  2. (ii).

    Removal of undesired tags

  3. (iii).

    Stop word removal

  4. (iv).

    Definition of slang terms

  5. (v).

    Stemming and lemmatization

  6. (vi).

    Part-of-speech tagging

  7. (vii).

    Coreference resolution

  8. (viii).

    Tag identification

Informal text (a.k.a. slang) and tag identification are the major contributions of proposed framework. Slangs and Tags play a significant role in the semantic orientation of sentiments and emotions. The performance of proposed framework is evaluated over Twitter dataset using Python natural language toolkit. Experimental setup revealed that the proposed system achieved promising results with an average F1-Measure of 71% and accuracy of 72.4%.

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Authors and Affiliations

  1. Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan

    Asad Habib & Arslan Ali Raza

  2. Department of Computer Sciences, Faculty of Sciences, COMSATS University Islamabad, Vehari, Pakistan

    Arslan Ali Raza

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  1. Asad Habib
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  2. Arslan Ali Raza
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Correspondence to Asad Habib .

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Editors and Affiliations

  1. Systems and Telecommunication, Santa Elena Provincial State University, Santa Elena, Ecuador

    Teresa Guarda

  2. Khyber Pakhtunkhwa, Centre for Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan

    Sajid Anwar

  3. Universidad Tecnológica Empresarial de Guayaquil, Guayaquil, Ecuador

    Marcelo Leon

  4. Instituto Politécnico de Leiria, Bidoeira de Cima, Portugal

    Filipe Jorge Mota Pinto

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Habib, A., Raza, A.A. (2022). IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications. In: Guarda, T., Anwar, S., Leon, M., Mota Pinto, F.J. (eds) Information and Knowledge in Internet of Things. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-75123-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-75123-4_10

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