We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jon-chun/sentimentarcs_notebooks | canonical | 2 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| get_ensemble_model_famalies | Ran | jon-chun/sentimentarcs_notebooks/utils/get_model_families.py code served (permissive licence) · get_code("dfcd02d9db4cff33") |
| get_lexsent_sentiment | Ran | jon-chun/sentimentarcs_notebooks/utils/get_sentiments.py code served (permissive licence) · get_code("1021dc822ac0f304") |
| get_fullpath | Not yet run | jon-chun/sentimentarcs_notebooks/utils/get_fullpath.py code served (permissive licence) · get_code("d018643f8372fc44") |
| sent2textblob | Not yet run | jon-chun/sentimentarcs_notebooks/utils/get_sentiments.py code served (permissive licence) · get_code("266d8515e74ff5d7") |
| sent2vader_comp | Not yet run | jon-chun/sentimentarcs_notebooks/utils/get_sentiments.py code served (permissive licence) · get_code("9a99dfee231a1e7b") |
| textfile2df | Not yet run | jon-chun/sentimentarcs_notebooks/utils/file_utils.py code served (permissive licence) · get_code("aaf6a2e4505bd0e9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
SOTA Transformer and DNN short text sentiment classifiers report over 97% accuracy on narrow domains like IMDB movie reviews. Real-world performance is significantly lower because traditional models overfit benchmarks and generalize poorly to different or more open domain texts. This paper introduces SentimentArcs, a new self-supervised time series sentiment analysis methodology that addresses the two main limitations of traditional supervised sentiment analysis: limited labeled training datasets and poor generalization. A large ensemble of diverse models provides a synthetic ground truth for self-supervised learning. Novel metrics jointly optimize an exhaustive search across every possible corpus:model combination. The joint optimization over both the corpus and model solves the generalization problem. Simple visualizations exploit the temporal structure in narratives so domain experts can quickly spot trends, identify key features, and note anomalies over hundreds of arcs and millions of data points. To our knowledge, this is the first self-supervised method for time series sentiment analysis and the largest survey directly comparing real-world model performance on long-form narratives.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.09454")
get_code_for_paper("2110.09454")
have("2110.09454")
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