Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank

Li, Quanzhi; Shah, Sameena; Nourbakhsh, Armineh; Liu, Xiaomo; Fang, Rui

doi:10.1145/2983323.2983915

Cited by 24 publications

(18 citation statements)

References 14 publications

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“…By considering structured and unstructured data with different semantics, Zhang [41] proposed an integrated framework, called collaborative knowledge base embedding (CKE), to learn the implicit representations and semantic representations in terms of collaborative filtering strategy. Based on the semantics of tags, Li [42] categorized tags into emotional types and developed an emotion ontology called UniEmotion for music recommendation. Although the content information has been successfully used for various recommender systems, sentimental information is also worthy of attention for capturing a personal user profile and improving the accuracy of document recommendations.…”

Section: Recommendation Approachesmentioning

confidence: 99%

Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng

Wang

2018

Scientific Programming

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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users' implicit interests. Sentiments on topics imply users' sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.

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Section: Recommendation Approachesmentioning

confidence: 99%

Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng

Wang

2018

Scientific Programming

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“…The C&W model and the word2vec model are the two popular word embedding models (Collobert et al, 2011;Mikolov et al, 2013). Word embeddings have been used in many NLP tasks (Socher et al, 2014;Mass, 2012;Matt, 2015;Tang et al, 2014b;Li et al, 2016;Li et al, 2017;Vo and Zhang, 2015). Although there are quite a few studies on word embedding for Twitter data, there is no previous study on word embeddings for stock market.…”

Section: Related Studiesmentioning

confidence: 99%

Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits

Li¹,

Shah²

2017

Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

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Previous studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financial domain and stock market. In this paper, we present a new approach to learning stock market lexicon from StockTwits, a popular financial social network for investors to share ideas. It learns word polarity by predicting message sentiment, using a neural network.The sentiment-oriented word embeddings are learned from tens of millions of StockTwits posts, and this is the first study presenting sentiment-oriented word embeddings for stock market. The experiments of predicting investor sentiment show that our lexicon outperformed other lexicons built by the state-of-the-art methods, and the sentiment-oriented word vector was much better than the general word embeddings.

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“…Pointwise mutual information (and its normalised variant npmi) is a common association measure to estimate topic coherence (Newman et al, 2010b;Mimno et al, 2011;Aletras and Stevenson, 2013;Lau et al, 2014;Fang et al, 2016). Although the method is successful in assessing topic quality, it tells us little about the association between documents and topics.…”

Section: Topic-level Evaluation: Topic Coherencementioning

confidence: 99%

“…Test data perplexity is the obvious solution, but it has been shown to correlate poorly with direct human assessment of topic model quality (Chang et al, 2009), motivating the need for automatic topic model evaluation methods which emulate human assessment. Research in this vein has focused primarily on evaluating the quality of individual topics (Newman et al, 2010b;Mimno et al, 2011;Aletras and Stevenson, 2013;Lau et al, 2014;Fang et al, 2016) and largely ignored evaluation of topic allocations to individual documents, and it has become widely accepted that topic-level evaluation is a reliable indicator of the intrinsic quality of the overall topic model (Lau et al, 2014). We challenge this assumption, and demonstrate that topic model evaluation should operate at both the topic and document levels.…”

Section: Introductionmentioning

confidence: 99%

“…Camacho-Collados et al (2016) increased the coverage of these WordNetbased approaches by exploiting the complementary knowledge of WordNet and Wikipedia along with pre-trained word embeddings. Finally, Wang et al (2014) and Fang et al (2016) proposed a model to align vector spaces of words and entities from knowledge bases. However, these approaches are restricted to nominal instances only (i.e.…”

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confidence: 99%

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