#textanalysis — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #textanalysis, aggregated by home.social.
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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
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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
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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
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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
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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
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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 -
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 -
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 -
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 -
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 -
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Fast Concordance: Instant concordance on a corpus of >1,200 books
https://iafisher.com/concordance/
#HackerNews #FastConcordance #InstantConcordance #Books #Corpus #TextAnalysis #LiteratureTech
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Fast Concordance: Instant concordance on a corpus of >1,200 books
https://iafisher.com/concordance/
#HackerNews #FastConcordance #InstantConcordance #Books #Corpus #TextAnalysis #LiteratureTech
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Fast Concordance: Instant concordance on a corpus of >1,200 books
https://iafisher.com/concordance/
#HackerNews #FastConcordance #InstantConcordance #Books #Corpus #TextAnalysis #LiteratureTech
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Fast Concordance: Instant concordance on a corpus of >1,200 books
https://iafisher.com/concordance/
#HackerNews #FastConcordance #InstantConcordance #Books #Corpus #TextAnalysis #LiteratureTech
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Fast Concordance: Instant concordance on a corpus of >1,200 books
https://iafisher.com/concordance/
#HackerNews #FastConcordance #InstantConcordance #Books #Corpus #TextAnalysis #LiteratureTech
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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
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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
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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
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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
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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
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Recs for text analysis tools, without any or only minimal genai - Taguette, QDA Miner, what else? Bulk document (around 50 papers) common word analysis is what Im mainly looking for, as well as individual document labelling. Open source, free, Windows 10.
#QualitativeData #textanalysis #software #research #academia #academicchatter #opensource -
Recs for text analysis tools, without any or only minimal genai - Taguette, QDA Miner, what else? Bulk document (around 50 papers) common word analysis is what Im mainly looking for, as well as individual document labelling. Open source, free, Windows 10.
#QualitativeData #textanalysis #software #research #academia #academicchatter #opensource -
Recs for text analysis tools, without any or only minimal genai - Taguette, QDA Miner, what else? Bulk document (around 50 papers) common word analysis is what Im mainly looking for, as well as individual document labelling. Open source, free, Windows 10.
#QualitativeData #textanalysis #software #research #academia #academicchatter #opensource -
Recs for text analysis tools, without any or only minimal genai - Taguette, QDA Miner, what else? Bulk document (around 50 papers) common word analysis is what Im mainly looking for, as well as individual document labelling. Open source, free, Windows 10.
#QualitativeData #textanalysis #software #research #academia #academicchatter #opensource -
Recs for text analysis tools, without any or only minimal genai - Taguette, QDA Miner, what else? Bulk document (around 50 papers) common word analysis is what Im mainly looking for, as well as individual document labelling. Open source, free, Windows 10.
#QualitativeData #textanalysis #software #research #academia #academicchatter #opensource -
#5WAnalysis #5W #textanalysis #phânTíchVanBản #5YếuTố
Cần xác định 5Yếu tố: Ai - Gì - Ở Đâu - Khi nào - Vì sao khi phân tích văn bản? Tìm tool giúp phân tích logic, không suy diễn & tự động tìm nguồn kiểm chứng online. Bạn thường dùng phương pháp gì?(None: Bài đăng gốc thiếu thông tin cụ thể về nền tảng hay ví dụ thực tế)
https://www.reddit.com/r/LocalLLaMA/comments/1p80l7x/how_to_analyse_text_for_5w/
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New article in #JCLS 4(1)! 🎉
@dudarjulia & @christof introduce a method for evaluating measures of #distinctiveness ( #keyness ) using synthetically generated, fully controlled text data.
#CLS #TextAnalysis #Evaluation #NLP #NLG #LiteraryComputing #CCLS25
https://jcls.io/issue/118/info/ -
New article in #JCLS 4(1)! 🎉
@dudarjulia & @christof introduce a method for evaluating measures of #distinctiveness ( #keyness ) using synthetically generated, fully controlled text data.
#CLS #TextAnalysis #Evaluation #NLP #NLG #LiteraryComputing #CCLS25
https://jcls.io/issue/118/info/ -
New article in #JCLS 4(1)! 🎉
@dudarjulia & @christof introduce a method for evaluating measures of #distinctiveness ( #keyness ) using synthetically generated, fully controlled text data.
#CLS #TextAnalysis #Evaluation #NLP #NLG #LiteraryComputing #CCLS25
https://jcls.io/issue/118/info/ -
New article in #JCLS 4(1)! 🎉
@dudarjulia & @christof introduce a method for evaluating measures of #distinctiveness ( #keyness ) using synthetically generated, fully controlled text data.
#CLS #TextAnalysis #Evaluation #NLP #NLG #LiteraryComputing #CCLS25
https://jcls.io/issue/118/info/ -
New article in #JCLS 4(1)! 🎉
@dudarjulia & @christof introduce a method for evaluating measures of #distinctiveness ( #keyness ) using synthetically generated, fully controlled text data.
#CLS #TextAnalysis #Evaluation #NLP #NLG #LiteraryComputing #CCLS25
https://jcls.io/issue/118/info/ -
Charting Twain: Building a Character Interaction Graph with Quarkus, OpenNLP, and a local Ollama Model. Uncover hidden dynamics in Huckleberry Finn using Java, sentiment analysis, and modern NLP.
https://myfear.substack.com/p/text-analytics-quarkus-opennlp-huckleberry-finn
#Java #Quarkus #OpenLNP #TextAnalysis -
Charting Twain: Building a Character Interaction Graph with Quarkus, OpenNLP, and a local Ollama Model. Uncover hidden dynamics in Huckleberry Finn using Java, sentiment analysis, and modern NLP.
https://myfear.substack.com/p/text-analytics-quarkus-opennlp-huckleberry-finn
#Java #Quarkus #OpenLNP #TextAnalysis -
Charting Twain: Building a Character Interaction Graph with Quarkus, OpenNLP, and a local Ollama Model. Uncover hidden dynamics in Huckleberry Finn using Java, sentiment analysis, and modern NLP.
https://myfear.substack.com/p/text-analytics-quarkus-opennlp-huckleberry-finn
#Java #Quarkus #OpenLNP #TextAnalysis -
Charting Twain: Building a Character Interaction Graph with Quarkus, OpenNLP, and a local Ollama Model. Uncover hidden dynamics in Huckleberry Finn using Java, sentiment analysis, and modern NLP.
https://myfear.substack.com/p/text-analytics-quarkus-opennlp-huckleberry-finn
#Java #Quarkus #OpenLNP #TextAnalysis