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Learning Spherical Word Vectors for Opinion Mining and Applying on Hotel Reviews

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1351)

Abstract

Hotel reviews are an important driving factor for hotel business. They can benefit guests to make informed hotel selections, and hotels to tackle their deficiencies and better their performance. In this paper, we propose an opinion mining approach that is applied to hotel reviews. The approach combines both lexical and word vectors’ methods to classify a review. An opinion-oriented sphere of word vectors is constructed, where words correspond to vectors on the sphere. The locations of the vectors reflect the similarity relations among the corresponding words, and they are derived using a simple relaxation algorithm. The classification task requires only a small-sized lexicon of positive and negative seeds. To measure the performance of our approach, we used a data set of hotel reviews. Moreover, we have conducted a comparative analysis using word vectors from other models. The results obtained proves the efficiency of our approach, giving better results at a significant reduction in computation time.

Keywords

  • Opinion mining
  • Sentiment analysis
  • Hotel reviews
  • Customer reviews
  • Word vectors
  • Word embeddings

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Rizkallah, S., Atiya, A.F., Shaheen, S. (2021). Learning Spherical Word Vectors for Opinion Mining and Applying on Hotel Reviews. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_19

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