Text Mining: Classification, Clustering, and Applications

By Ashok Srivastava, Mehran Sahami. 2009

591 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/1fb5d667fb732a89b37da1e07d10fea9b3eb336c

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Citations in the corpus

Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation (2017, background): “This is especially important as the topics within articles, the main ideas within articles that can be shared among similar articles, cannot always be detected through traditional keyword searches [3].”
Exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy (2019, methodology): “Text mining usually includes text categorization, text clustering and sentiment analysis, among others (Srivastava & Sahami, 2009).”
topicmodels: An R Package for Fitting Topic Models (2011, methodology): “…addition the final document-term matrix can be reduced by selecting only the terms which occur in a minimum number of documents (see Griffiths and Steyvers 2004, who use a value of 5) or those terms with the highest term-frequency inverse document frequency (tf-idf) scores (Blei and Lafferty 2009).”
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency (2016, methodology): “In practice, these stop words are chosen using tf-idf (Blei and Lafferty, 2009).”
Processing Social Media Messages in Mass Emergency (2014, background): “Interested readers should refer to other sources for a broader discussion [Srivastava and Sahami 2009].”
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015, background): “Text mining is a common task in fields such as information retrieval (IR) systems [6], aimed to return a set of relevant documents for a given query, and some classification systems, as the ones used for spam filtering [7] in electronic mail services.”
A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, background): “One key decision in topic modeling is choosing the number of topics for a corpus if this parameter is not specified a priori (Blei and Lafferty 2009).”
Connotation Frames of Power and Agency in Modern Films (2017, background): “There is much prior research focus on bias in social media (Garcia et al., 2014; Prabhakaran et al., 2014; Ratkiewicz et al., 2011; Yano et al., 2010; Srivastava and Sahami, 2009), complementing our investigation on movies.”