Characterization of Written Languages Using Structural Features from Common Corpora

Rozz, Younis Al; Hamoodat, Harith; Menezes, Ronaldo

doi:10.1007/978-3-319-54241-6_14

Cited by 4 publications

(1 citation statement)

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“…Rather than using techniques resulting in word embeddings that abstract words into non-interpretable vectors, we retain the nature of the word itself and instead use tools from network science to analyze the word co-occurrence structure in the corpus. Although NLP has leaned more towards probabilistic models and neural networks, network science has nevertheless found a wide range of applications in computational linguistics (Ferrer i Cancho et al 2004;Liu and Cong 2013;Cong and Liu 2014;Al Rozz et al 2017;Wang et al 2017;Chen et al 2018;Jiang et al 2019). In fact, some techniques commonly used in NLP, such as latent Dirichlet allocation, have been shown to parallel network science techniques (Gerlach et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Sentiment and structure in word co-occurrence networks on Twitter

et al. 2022

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We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton’s presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus.

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