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Semantic Interpretation of Tweets: A Contextual Knowledge-Based Approach for Tweet Analysis

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Knowledge Computing and Its Applications

Abstract

Tweets are cryptic and often laced with insinuation. Hence, interpretation of tweets cannot be done in isolation. Human beings can interpret the tweets because they possess the requisite Contextual Knowledge. This knowledge enables them to understand the context of tweets and interpret the text. Emulating interpretation ability in machines requires the machine to acquire this contextual knowledge. Tweets pertaining to political and societal issues contain domain-specific terms. Interpretation of such tweets solely on the basis of sentiment orientation of words produces incorrect sentiment tags. Polarity of terms is based on the topic of reference. Thus, an understanding of the pertinent domain terms and their associated sentiment is essential to guide the sentiment mining process. A resource of relevant domain-specific contextual terms and associated sentiments can help to achieve an enhanced sentiment mining performance. With the objective of equipping the machine with the contextual knowledge to facilitate semantic interpretation, we tap the Web resources, process them and structure them as Contextual Knowledge Structures (CKS). We then leverage the CKS to enable a semantic interpretation of tweets. We construct a CKS-based training set to train the Naïve Bayes classifier and classify the tweets. We further transform the CKS into sentiment training set (STS) and use it for detecting sentiment polarity tags for tweets. CKS provide the necessary background knowledge pertaining to issues, events, and the related domain-specific terms, thus facilitating semantic sentiment mining. All our experiments are conducted in the context of political/public policy, trending topic, and event-related tweets with an objective of obtaining a pulse of the political climate in India. Our CKS-based classifier exhibits an accuracy of 94.23% in mapping the tweets to the political topic. The distance-based CKS-Sentiment mining algorithm exhibits a consistent performance with an accuracy of 70.90%. The relevance of this contribution is: (a) a novel method which leverages the Web content to derive an optimum training set for tweet analysis, (b) a high degree of Accuracy, Precision, and Recall in tweet classification and sentiment mining with a small CKS-based training set, (c) a topic-adaptive model which can adapt to any domain or topic and exhibit improved tweet analysis performance.

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Notes

  1. 1.

    Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of task/features that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job.

  2. 2.

    Chunking is the process of grouping various words which have Part-Of-Speech (POS) tags into phrases like Noun phrases, Verb phrases etc.

  3. 3.

    On February 9, 2016, students of Jawaharlal Nehru University (JNU) held a protest on their campus against the capital punishment meted out to the 2001 Indian Parliament attack convict Afzal Guru.

  4. 4.

    On 8 November 2016, the Government of India announced the demonetization of all ₹500 (US 7.80) and ₹1,000 (US 16) banknotes of the Mahatma Gandhi Series.

  5. 5.

    The Wu and Palmer [39] similarity metric is used to measure the depth of the given concepts in the Word Net taxonomy, the least common subsumer (LCS) depth and combines these figures into a similarity score.

  6. 6.

    SentiWordNet is a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity and objectivity. It gives scores in the range [0,1] for each of the sentiments i.e. positive_score + negative_score + objective_score = 1.

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Authors and Affiliations

  1. Department of Computer Science and Applications, Bangalore University, Bengaluru, India

    Nazura Javed & Muralidhara B. L.

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  1. Nazura Javed
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  2. Muralidhara B. L.
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Correspondence to Nazura Javed .

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Editors and Affiliations

  1. Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    S. Margret Anouncia

  2. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Uffe Kock Wiil

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Javed, N., B. L., M. (2018). Semantic Interpretation of Tweets: A Contextual Knowledge-Based Approach for Tweet Analysis. In: Margret Anouncia, S., Wiil, U. (eds) Knowledge Computing and Its Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-6680-1_4

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  • DOI: https://doi.org/10.1007/978-981-10-6680-1_4

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