Detecting Emotions in Social Media: A Constrained Optimization Approach
Authors: Yichen Wang, Aditya Pal
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our model through three diverse real world datasets, and show that it outperforms existing state-of-art methods for emotion detection. |
| Researcher Affiliation | Collaboration | Yichen Wang Aditya Pal Georgia Institute of Technology IBM Research Atlanta, GA San Jose, CA yichen.wang@gatech.edu aditya.pal@gmail.com |
| Pseudocode | Yes | Algorithm 1 MINIMIZE Ψ 1: Initialize S(0), D(0), u(0), v(0), A(0), B(0) randomly 2: t = 0 3: repeat 4: t = t + 1 5: Compute S(t) using multiplicative rule (Eq 5). 6: Similarly compute D(t), u(t), v(t), A(t), B(t). 7: until Ψ(t 1) Ψ(t) ϵ or t max Iteration 8: return S(t), D(t), u(t), v(t), A(t), B(t) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Sem Eval2: This dataset consists of 1250 news headlines annotated by human coders on six emotions... http://web.eecs.umich.edu/~mihalcea/downloads.html |
| Dataset Splits | Yes | We use 10-fold cross validation to run our experiments and report precision (P), recall (R), and F-score (F). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions mathematical models and algorithms (e.g., NMF, SVM, multiplicative update rule) but does not specify any software libraries or their version numbers used in the implementation. |
| Experiment Setup | Yes | We use cosine similarity between the documents features to compute their topic similarity and set threshold τ=0.8. |