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  • 1.
    Jamatia, Anupam
    et al.
    National Institute of Technology Agartala, India.
    Swamy, Steve
    National Institute of Technology Agartala, India; NTNU Norwegian University of Science and Technology, Norway.
    Gambäck, Björn
    RISE Research Institutes of Sweden, Digital Systems, Data Science. National Institute of Technology Agartala, India.
    Das, Amitava
    Wipro Ai Labs, India.
    Debbarma, Swapam
    National Institute of Technology Agartala, India.
    Deep Learning Based Sentiment Analysis in a Code-Mixed English-Hindi and English-Bengali Social Media Corpus2020In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 29, no 5, article id 2050014Article in journal (Refereed)
    Abstract [en]

    Sentiment analysis is a circumstantial analysis of text, identifying the social sentiment to better understand the source material. The article addresses sentiment analysis of an English-Hindi and English-Bengali code-mixed textual corpus collected from social media. Code-mixing is an amalgamation of multiple languages, which previously mainly was associated with spoken language. However, social media users also deploy it to communicate in ways that tend to be somewhat casual. The coarse nature of social media text poses challenges for many language processing applications. Here, the focus is on the low predictive nature of traditional machine learners when compared to Deep Learning counterparts, including the contextual language representation model BERT (Bidirectional Encoder Representations from Transformers), on the task of extracting user sentiment from code-mixed texts. Three deep learners (a BiLSTM CNN, a Double BiLSTM and an Attention-based model) attained accuracy 20-60% greater than traditional approaches on code-mixed data, and were for comparison also tested on monolingual English data.

  • 2.
    Papatheocharous, Efi
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Belk, Marios
    University of Cyprus, Cyprus.
    Germanakos, Panagiotis
    University of Cyprus, Cyprus; SAP AG, Germany.
    Samaras, George
    University of Cyprus, Cyprus.
    Towards implicit user modeling based on artificial intelligence, cognitive styles and web interaction data2014In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 23, no 2Article in journal (Refereed)
    Abstract [en]

    A key challenge of adaptive interactive systems is to provide a positive user experience by extracting implicitly the users' unique characteristics through their interactions with the system, and dynamically adapting and personalizing the system's content presentation and functionality. Among the different dimensions of individual differences that could be considered, this work utilizes the cognitive styles of users as determinant factors for personalization. The overarching goal of this paper is to increase our understanding about the effect of cognitive styles of users on their navigation behavior and content representation preference. We propose a Web-based tool, utilizing Artificial Intelligence techniques, to implicitly capture and find any possible relations between the cognitive styles of users and their characteristics in navigation behavior and content representation preference by using their Web interaction data. The proposed tool has been evaluated with a user study revealing that cognitive styles of users have an effect on their navigation behavior and content representation preference. Research works like the reported one are useful for improving implicit and intelligent user modeling in engineering adaptive interactive systems.

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