The main goal of this notebook is to demonstrate the use of semi supervised data labelling technique to label news about cleantech in accordance with UN Sustainable Goal 13 - Climate Action and 7 - Affordable and Clean Energy.
Techcrunch news article snapshot of 2011-2021 with 32,000 news. All copyright belongs to TechCrunch.
For validation data, 3,000 news were labelled manually with 2 categories: CLEANTECH and NOTRELEVANT
- date_gmt(datetime) - published date
- link(string) - source article link
- clean_text (string) - news article summary, concatenated with header
- categories (string) - article categories ids
- tags (string) - tag ids assigned to the article
- author(digit) - the reporter id who is the author of the news article
- _embedded(string) - string with dictionaries that contain some descriptive info on authors, categories and tags
- All news tags and categories were analyzed with
calc_count_stats.pyscript, and were divided into cleantech/notrelevant. - Data were enriched with unsupervised topics model using Gensim with
gensim_model.pyscript. Topics were then used as one of the inputs in snorkel functions. - Snorkel functions focused on: keywords matching, cleantech/not cleantech abbrevations, news categories, news tags, cleantech funds mentioned, mentions of cleantech/not cleantech startups, authors that write on topics not in cleantech.
| function name | j | Polarity | Coverage | Overlaps | Conflicts | Correct | Incorrect | Emp. Acc. |
|---|---|---|---|---|---|---|---|---|
| lf_cleantech_keywords | 0 | [0] | 0.021927 | 0.018605 | 0.010299 | 32 | 34 | 0.484848 |
| lf_notcleantech_keywords | 1 | [1] | 0.001661 | 0.001661 | 0.000000 | 5 | 0 | 1.000000 |
| lf_contains_cleantech_abbrv | 2 | [0] | 0.019269 | 0.014618 | 0.007641 | 36 | 22 | 0.620690 |
| lf_contains_notcleantech_abbrv | 3 | [1] | 0.023256 | 0.021595 | 0.000997 | 69 | 1 | 0.985714 |
| lf_cleantech_startups | 4 | [] | 0.000000 | 0.000000 | 0.000000 | 0 | 0 | 0.000000 |
| lf_notcleantech_startups | 5 | [1] | 0.011296 | 0.007973 | 0.001329 | 33 | 1 | 0.970588 |
| lf_cleantech_funds | 6 | [] | 0.000000 | 0.000000 | 0.000000 | 0 | 0 | 0.000000 |
| lf_contains_notcleantech_categories | 7 | [1] | 0.273422 | 0.246512 | 0.008970 | 821 | 2 | 0.997570 |
| lf_contains_cleantech_categories | 8 | [0] | 0.025249 | 0.019269 | 0.008306 | 62 | 14 | 0.815789 |
| lf_contains_cleantech_tags | 9 | [0] | 0.074751 | 0.052824 | 0.031561 | 127 | 98 | 0.564444 |
| lf_contains_notcleantech_tags | 10 | [1] | 0.638538 | 0.379734 | 0.035880 | 1877 | 45 | 0.976587 |
| lf_notcleantech_authors | 11 | [1] | 0.052824 | 0.046844 | 0.001661 | 155 | 4 | 0.974843 |
| lf_notcleantech_topics | 12 | [1] | 0.224252 | 0.187375 | 0.009635 | 664 | 11 | 0.983704 |
produces a set of noise-aware probibalistic training labels
| Measure | Value |
|---|---|
| accuracy | 0.949 |
| recall | 0.95 |
| precision | 0.99 |
takes the most popular label from functions on per datapoint basis
| Measure | Value |
|---|---|
| accuracy | 0.964 |
| recall | 0.966 |
| precision | 0.99 |
To run the notebook execute poetry install and then poetry run jupyter lab.