Mining Text Data

By Charu C. Aggarwal, Chengxiang Zhai. Springer US, 2012

1,271 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/0efa2632f2e1edc5c0383f7b4dc257e2806032a0

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Citations in the corpus

A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “Named entity recognition is a preprocessing step in the relation extraction task and also has other applications such as in question answering [1, 89].”
Data Classification: Algorithms and Applications (2014, neutral): “The combination of text with multimedia data in order to create more effective classification models has been discussed in [8].”
Social media big data analytics: A survey (2019, neutral): “Some of the most common techniques are classi ﬁ cation (Charu C Aggarwal & Zhai, 2012; Reuter & Cimiano, 2012), clustering (Lim, Tucker, & Kumara, 2017; Tang & Liu, 2012), and deep learning (Jansson & Liu, 2017; D. T.; Nguyen, Joty, Imran, Sajjad, & Mitra, 2016).”
Text Classification Algorithms: A Survey (2019, methodology): “is formulation jointly maximizes the log-likelihood of the forward and backward directions as follows: N å k=1 0 @ log p(t kjt1,. . .,t k 1;Qx, ! QLSTM,Qs)+ log p(t kjt k+1,. . .,tN;Qx, QLSTM,Qs) 1 A (8) where Qx is the token representation and Qx refers to the softmax layer. Then, ELMo is computed ”
Sentiment analysis algorithms and applications: A survey (2014, background): “Some of the current features are [62]: Terms presence and frequency : These features are individual words or word n-grams and their frequency counts.”
Pseudo-Relevance Feedback Based on Matrix Factorization (2016, positive): “𝑤𝑒𝑖𝑔ℎ𝑡 𝑤, 𝑑 = 𝑇𝐹 𝑤, 𝑑 × 𝐼𝐷𝐹(𝑤) Similar to the language modeling implementation of RFMF, the feedback vector can be obtained using the first row of ෠𝑅 .”
Forecasting: theory and practice (2020, background): “Forecasting with text information64 Text data, such as social media posts, scholar articles and company reports, often contains valuable information that can be used as predictors in forecasting models (Aggarwal and Zhai, 2012).”
Smart Anomaly Detection in Sensor Systems (2020, methodology): “…[43],[44] Parametric [3],[14],[19],[20] Information theory [47],[48],[3] Spectral [45],[46],[3] Data-driven techniques Supervised [49],[50],[51],[52],[53],[54] Deep RBM [80],[56],[81],[82],[83] Semi-supervised [55],[56],[57],[58] CNN [68],[69] Unsupervised [59],[60],[61] GAN…”
Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, background): “With the rapid development in the fields of Text Mining (TM) and Natural Language Processing (NLP), the study on Event Detection (ED) has gained great popularity (Aggarwal and Zhai 2012; Hirschberg and Manning 2015).”
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015, background): “Since the text of raw posts cannot be used to build such a classifier, a prior text mining process [5] has to be applied.”