Inferring Latent User Properties from Texts Published in Social Media
Authors: Svitlana Volkova, Yoram Bachrach, Michael Armstrong, Vijay Sharma
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rely on machine learning and natural language processing techniques to learn models from user communications. We first examine individual tweets to detect emotions and opinions emanating from them, and then analyze all the tweets published by a user to infer latent traits of that individual. ... Our models outperform the state-of-the-art approaches for inferring various traits, mostly due to to better feature engineering and the larger training data size for commonly explored attributes. |
| Researcher Affiliation | Collaboration | Svitlana Volkova1, Yoram Bachrach2, Michael Armstrong2 and Vijay Sharma2 1Center for Language and Speech Processing, Johns Hopkins University, Baltimore MD 21218, USA svitlana@jhu.edu 2Microsoft Research, Cambridge CB1 2FB, UK {yobach, a-mica, a-vishar}@microsoft.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., specific repository link, explicit code release statement, or code in supplementary materials). |
| Open Datasets | No | The paper describes creating a dataset via crowdsourcing ('We asked workers on Amazon Mechanical Turk to glance through 5,000 Twitter profiles...') and mentions using 'noisy hashtag annotations', but does not provide concrete access information (e.g., link, DOI, repository, or formal citation to the created dataset) for public availability. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions training 'log-linear models' and using 'lexical features' but does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |