Abstract
In recent decades, microblogs generate large volumes of data in the form of short text. Twitter has been one of the most widely used microblogging sites. Twitter data consist of noise due to shortness, which need to be preprocessed to find the accurate sentiment expressed by the user. The major challenges in short texts are the presence of noisy data like URLs, misspelling, slang words, repeated characters, punctuation, etc. To handle these challenges, this paper proposes to combine various preprocessing techniques with different classification methods as a tool for Twitter sentiment analysis. We evaluated the effect of noisy data like URLs, hashtags, negations, repeated characters, punctuations, stopwords and stemming. We use n-gram representation model to find the bindings and further applied support vector machine (SVM) and K-nearest neighbors (KNN) multi-class classifiers for sentiment classification. Experiments are conducted to observe the effect of various preprocessing techniques on Stanford Twitter Sentiment Dataset. The extensive experimental results are presented to show the effect of various preprocessing techniques to classify short texts.
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Keerthi Kumar, H.M., Harish, B.S. (2018). Classification of Short Text Using Various Preprocessing Techniques: An Empirical Evaluation. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_3
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DOI: https://doi.org/10.1007/978-981-10-8633-5_3
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