Exploiting Social Network Structure for Person-to-Person Sentiment                     Analysis

West, Robert; Paskov, Hristo S.; Leskovec, Jure; Potts, Christopher

doi:10.1162/tacl_a_00184

Cited by 153 publications

(95 citation statements)

References 24 publications

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“…Crane & Dempsey keep only the links whose weights are above a predefined threshold [12]. For signed networks, it is possible to consider the absolute value of the weights [34,54,80]. In addition to this, Figueiredo & Frota create both positive and negative links between the same two nodes when their agreement and disagreement levels are both above some threshold [34].…”

Section: Referencesmentioning

confidence: 99%

Multiple partitioning of multiplex signed networks

2020

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For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated to their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset.

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Section: Referencesmentioning

confidence: 99%

Multiple partitioning of multiplex signed networks

2020

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“…• Weibo-STC: Our proposed Weibo Sentiment Towards Celebrities dataset consists of three heterogeneous networks with 12,814 users, 126,380 tweets, 71,268 social links and 37,689 profile values, of which the detail is presented in Section 3. • Wiki-RfA: Wikipedia Requests for Adminship [30] is a signed network with 10,835 nodes and 159,388 edges, corresponding to votes cast by Wikipedia uses in election for promoting individuals to the role of administrator. A signed link indicates a positive or negative vote by one user on the promotion of another.…”

Section: Datasetsmentioning

confidence: 99%

Shine

Wang

Zhang

Hou

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

287

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In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg" about users' true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users' profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users' latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.

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“…With the inclusion of these embeddings, their convolutional neural network (CNN) achieves a 2% gain in accuracy over that of Bamman and Smith. Ghosh and Veale (2017) present a combination CNN/LSTM (long short-term memory RNN) architecture that takes as inputs user affect inferred from recent tweets as well as the text of the tweet and that of the parent tweet. When a tweet was addressed to someone by name, the name of the addressee was included in the text representation of the tweet, providing a loose link between interlocutors (West et al, 2014) and a ≈1% gain in performance for some data sets.…”

Section: Previous Workmentioning

confidence: 99%

Representing Social Media Users for Sarcasm Detection

Kolchinski¹,

Potts²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.

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Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Cited by 153 publications

References 24 publications

Multiple partitioning of multiplex signed networks

Multiple partitioning of multiplex signed networks

Shine

Representing Social Media Users for Sarcasm Detection

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