Time-Sensitive Opinion Mining for Prediction

Authors: Wenting Tu, David Cheung, Nikos Mamoulis

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We conduct an experimental evaluation using a collection of microblogs posted by investors to demonstrate the effectiveness of our approach.
Researcher Affiliation Academia Wenting Tu, David Cheung, and Nikos Mamoulis Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong
Pseudocode No No pseudocode or clearly labeled algorithm blocks were found in the paper.
Open Source Code No The paper mentions a 'Complementary-material webpage: http://sites.google.com/site/wenting4aaai/' and states 'More details about the experimental setup and the competitor approaches are on our complementary-material webpage.', but it does not explicitly state that the source code for the methodology is released or available at this link.
Open Datasets No The paper mentions 'a collection of microblogs posted by investors' and '50,169 microblogs of investors posted on Sina Weibo', but does not provide specific access information (link, DOI, formal citation) for a publicly available or open dataset.
Dataset Splits No The paper mentions '1,000 manually labeled (future or non-future) opinions' and the use of '50,169 microblogs', but it does not provide specific training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits).
Hardware Specification No No specific hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running experiments were mentioned in the paper.
Software Dependencies No No specific software dependencies with version numbers were mentioned in the paper.
Experiment Setup No The paper states 'More details about the experimental setup and the competitor approaches are on our complementary-material webpage.', indicating that specific details are not provided in the main text of the paper.