Abstract
Sentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues. The generated representations are further ensembled and classified using a neural-based early fusion approach. Further, we propose a centrality aware random-walk for node embedding and tweet representations suitable for the multi-layer network. From various experimental analysis, it is evident that the proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better classification performance than the text-based counterparts. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.
Highlights
With the growing popularity of Twitter, sentiment analysis of tweets has drawn the attention of several researchers from both academia and industry in recent times
We investigate the efficacy of our proposed multilayer network using four different types of node embedding methods namely Multiplex Network Embedding (MNE) (Zhang et al, 2018), MultiView Embedding (MVE) (Qu et al, 2017), FastText (FT) (Bojanowski et al, 2017), and Sentiment Hashtag Embedding (SHE) (Singh et al, 2020)
We investigate three random walk methods to generate the node sequences, namely Unbiased random walk used in MNE, biased random walk used in Node2Vec (N2V) (Grover and Leskovec, 2016) and the proposed centrality aware Biased random walk
Summary
With the growing popularity of Twitter, sentiment analysis of tweets has drawn the attention of several researchers from both academia and industry in recent times. Researchers try to address these problems by adopting various methods like task-specific representation learning (Singh et al, 2020; Pham and Le, 2018; Fu et al, 2018; Tang et al, 2016; Kim, 2014), incorporating additional information such as hash-. This paper proposes a novel approach to handle the above issues using a heterogeneous multi-layer network representation of a tweet. A heterogeneous multi-layer network can be formed by connecting layers of networks of mentions, hashtags, and keywords. (i) The semantic relation between keywords, hashtags, and mentions can be captured by applying an effective network embedding method. The co-occurring keywords, hashtags, and mentions often share semantic relationships (Wang et al, 2016; Weston et al, 2014; Qadir and Riloff, 2013; Wang et al, 2011)
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