Short Text Representation for Detecting Churn in Microblogs
Authors: Hadi Amiri, Hal Daume III
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Twitter data about three telco brands show the utility of our approach for this task. |
| Researcher Affiliation | Academia | Hadi Amiri and Hal Daum e III Computational Linguistics and Information Processing (CLIP) Lab Institute for Advanced Computer Studies University of Maryland {hadi,hal}@umiacs.umd.edu |
| Pseudocode | No | The paper provides mathematical equations for the RNN model but does not include any pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We utilize churn data provided by (Amiri and Daume III 2015)3. The data was collected from twitter for three telecommunication brands: Verizon, T-Mobile, and AT&T. Footnote 3: www.umiacs.umd.edu/ hadi/ch Data/ |
| Dataset Splits | Yes | We performed all the experiments through 10-fold cross validation and used the twotailed paired t-test ρ < 0.01 for significance testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the "Vowpal Wabbit classification toolkit" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We use our development dataset to learn our RNN model for tweet representation. For this, we set the size of hidden layer to m = 128 in the experiments. We employ Vowpal Wabbit classification toolkit4 with all parameters set to their default values to perform the classification experiments. |