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Topic-Based Sentiment Analysis

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Information Management and Big Data (SIMBig 2015, SIMBig 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 656))

Abstract

We present a method that exploits syntactic dependencies for topic-oriented sentiment analysis in tweets. The proposed solution is based on supervised text classification and available polarity lexicons in order to identify the relevant dependencies in each sentence by detecting the correct attachment points for the polarity words. Our experiments are based on the data from the Semantic Evaluation Exercise 2015 (SemEval-2015), task 10, subtask C. The dependency parser that we used is adapted to this kind of text. Our classifier that combines topic- and sentence-level features obtained very good results.

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Notes

  1. 1.

    We did not participate in the task, we downloaded the data after the evaluation campaigned.

  2. 2.

    Comparing the weighted average F1 measure, the results obtained using a t-test with both sentence- and topic-level features for decision trees (0.64) was noticeably higher than SVM (0.60) and statistically significant than Naive Bayes algorithm (0.44).

  3. 3.

    Decision trees macro average F1 measure (0.48) was substantially higher than both SVM (0.39) and Naive Bayes (0.35) macro average F1 measure.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, K1N6N5, Canada

    Prasadith Buddhitha & Diana Inkpen

Authors
  1. Prasadith Buddhitha
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  2. Diana Inkpen
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Correspondence to Prasadith Buddhitha .

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Editors and Affiliations

  1. College of Medicine, University of Florida, Gainesville, Florida, USA

    Juan Antonio Lossio-Ventura

  2. Faculty of Engineering, Universidad del Pacífico, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

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Buddhitha, P., Inkpen, D. (2017). Topic-Based Sentiment Analysis. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-55209-5_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-55209-5

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