Poking around with sentiment analysis on the public domain copy of Pride and Prejudice by Jane Austen.

I extracted the speech, did a strict attribution, and ran sentiment analysis for different speakers based off chunks sampled from the text.

Elizabeth is neutral with a 28% confidence level, Jane is joyful at a 57% confidence. Darcy is sad with 94% confidence and Mrs Bennet is joyful at 95% confidence.

Those aren't the emotions I get from reading the text. Again, I'm learning more about the sentiment analysis than the text.
https://www.kaggle.com/code/alisonhawke/pride-and-prejudice

#DataScience #Python #Literature #TextAnalysis #SentimentAnalysis

Spent some time doing data analysis on the Project Gutenberg text of Pride and Prejudice.

Pulling out all the speech, the library I used said it was "emotionally neutral" in sentiment. Which is interesting because when you read it, the speech is absolutely not that. There's a lot in the subtleties of the speech that makes it very pointed.

The confidence on the emotional rating was 57%, which seems low to me. Doing analysis on a book I'm familiar with and recently read is telling me as much about the means of evaluating the text as the text itself.
#DataScience #TextAnalysis #SentimentAnalysis

Metatextual Literacy

https://www.jenn.site/metatextual-literacy/

#HackerNews #metatextualliteracy #digitalliteracy #criticalreading #mediaawareness #textanalysis

新清士@(生成AI)インディゲーム開発者 (@kiyoshi_shin)

일본어 트윗으로, 'Opus'라는 모델이 AI 생성 문장을 탐지하고 인간이 수정한 부분과 그대로 남긴 부분의 차이를 식별할 수 있다고 설명함. 이 모델은 언어 스타일 분석 및 AI 탐지 능력을 갖춘 새로운 텍스트 분석 기술로 보임.

https://x.com/kiyoshi_shin/status/2025339820688769277

#ai #nlp #textanalysis #opus #aigeneration

Why do politicians always talk about "middle class," "immigrants," or "families"?

New research funded by @fwf and @dfg_public led by Dr. Lena Maria Huber (https://lenamariahuber.eu/, MZES, University of Mannheim) and Dr. Hauke Licht (University of Innsbruck), explores how politicians talk about social groups in campaign platforms and parliamentary speeches across 8 Western European countries.

🔗https://haukelicht.github.io/projects/gaepd/

#PoliticalCommunication #ComputationalSocialScience #Democracy #TextAnalysis

Can #AI reasoning models infer people's underlying reasons in unstructured chat data from group decisions?

Across multiple prompting steps, #GTP5 usually did NOT select the same underlying reason as a human rater: https://doi.org/10.48550/arXiv.2601.05582

#AI #cogSci #textAnalysis #psychometrics

Fast Concordance: Instant concordance on a corpus of >1,200 books

https://iafisher.com/concordance/

#HackerNews #FastConcordance #InstantConcordance #Books #Corpus #TextAnalysis #LiteratureTech

Ive been digging around for text analysis OS apps and found AntConc via a Reddit thread. This app is very good from what I can see in early quick testing. Im looking at term frequency across relevant papers, and some 'concordance' context but AntConc will do a lot more. Together with Taguette you have all you need for a lot of analysis.

Im running portable on Windows but Mac and Linux also work.
https://www.laurenceanthony.net/software/antconc/

#AntConc #textanalysis #research #academia #academicchatter #linguistics

Discover google/langextract, a game-changing Python library for extracting insights from unstructured text with LLMs! #LLM #NLP #TextAnalysis

The google/langextract library leverages Large Language Models (LLMs) to extract structured information from unstructured text, enabling precise source grounding and interactive visualization capabilities. By utilizing Python, developers can integrate this...

#google/langextract #LargeLanguageModels #NaturalLanguageProcessing #PythonLibraries