Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Segmentation of Tweets with URLs and its Applications to Sentiment Analysis
Authors: Abdullah Aljebreen, Weiyi Meng, Eduard Dragut12480-12488
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an extensive empirical evaluation of our approach in this section to show (i) the effectiveness of our segmentation algorithm and (ii) its beneο¬t to sentiment analysis on tweets. |
| Researcher Affiliation | Academia | 1 Temple University 2 Binghamton University |
| Pseudocode | Yes | Algorithm 1: The pseudo code of the main function of our algorithm. |
| Open Source Code | No | The paper mentions using a third-party library 'Twitter4J1' and provides its URL, but it does not provide an explicit statement or link to the open-source code for their own developed methodology. |
| Open Datasets | Yes | We run our SA experiments on two datasets: ... (2) Sem Eval is a collection of 60k labeled tweets for the Sem Eval tasks (2013-2017) (Rosenthal, Farra, and Nakov 2017). |
| Dataset Splits | No | The paper describes sampling methods (simple random and stratified random sampling) to approximate accuracy via manual inspection of 1,000 tweets, but it does not specify traditional training/validation/test dataset splits used for model development or hyperparameter tuning. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Twitter4J1' and 'Stanford Core NLP, BERT' tools, but it does not provide specific version numbers for these or other software dependencies used in their implementation. |
| Experiment Setup | No | The paper describes its greedy algorithm and some general steps (e.g., 'similarity threshold'), but it does not provide specific numerical hyperparameters (like learning rates, batch sizes, epochs) or detailed system-level training configurations for their experiments. |