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Opinion Classification via Word and Emoji Embedding Models with LSTM

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Speech and Computer (SPECOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12997))

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Abstract

As social networks are rapidly growing, the content created in them is also growing. Mining the emotional tendency of comments on this content through opinion classification technologies is very useful for the timely understanding of public opinion on social media, monitoring of brands, and customer support. Deep learning methods have shown good results in opinion classification. In this paper, we analyze the opinion classification in Uzbek movie reviews taken from YouTube using various pre-trained word embedding models and a classification model based on long short-term memory. Users often use emojis along with text to express their opinions and feelings. Therefore, we also investigated the importance of emojis in opinion classification of Uzbek texts.

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Author information

Authors and Affiliations

  1. Faculty of Digital Technologies, Samarkand State University, Samarkand, Uzbekistan

    Ilyos Rabbimov & Sami Kobilov

  2. School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK

    Iosif Mporas

Authors
  1. Ilyos Rabbimov
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  2. Sami Kobilov
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  3. Iosif Mporas
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Corresponding author

Correspondence to Ilyos Rabbimov .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Rabbimov, I., Kobilov, S., Mporas, I. (2021). Opinion Classification via Word and Emoji Embedding Models with LSTM. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_53

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  • DOI: https://doi.org/10.1007/978-3-030-87802-3_53

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  • Online ISBN: 978-3-030-87802-3

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