Pull out all the stops: Textual analysis via punctuation sequences

Darmon, Alexandra N. M.; Bazzi, Marya; Howison, Sam; Porter, Mason A.

doi:10.31235/osf.io/2rzsg

Cited by 3 publications

(2 citation statements)

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“…Text tokenisation: Each sentence is transformed into a sequence of tokens, i.e., words, punctuation and special characters (e.g., emojis). Despite punctuation being relevant in some cases, e.g., writing style analysis 85 , we discard it here and focus only on words and other tokens with emotional content like emojis.…”

Section: Text Analysis In Emoatlasmentioning

confidence: 99%

EmoAtlas: An emotional profiling tool merging psychological lexicons, artificial intelligence and network science

Semeraro

Vilella

Mohammad³

et al. 2023

Preprint

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This work introduces EmoAtlas as a computational framework extracting emotions and syntactic/semantic word associations automatically from texts. EmoAtlas implements a cutting-edge synergy of interpretable artificial intelligence (AI) and psychologically-validated lexicons. Our framework supports out-of-the-box emotion detection of 8 categorical emotions and syntactic network building in 18 languages. In the task of detecting emotions in human-labelled social media posts and news media articles, either in Italian or in English, EmoAtlas achieves performances analogous, or even superior (e.g., 70.2% precision for detecting joy in tweets), to state-of-art natural language processing techniques like BERT, RoBERTa, distillBERT and ALBERT (e.g., 67.9% precision for detecting joy in tweets). This performance comes with EmoAtlas being 12 times faster than BERT. In a psychometric task like reproducing human creativity ratings for 1,071 short stories, EmoAtlas and BERT obtain equivalent predictive power (ρ ≈0.495, p<10-4). Combining BERT's semantic features with EmoAtlas' emotional/syntactic networks of words, the cross-validated resulting AIs get substantially better at estimating creativity rates of stories (ρ ≈0.628, p<10-4). This indicates an interplay between the creativity of narratives and the semantic, emotional, and syntactic divergence of their words, pointing out how EmoAtlas and BERT-like models could be used in synergy in psychometrics. By outputting interpretable emotional profiles and syntactic networks, EmoAtlas can quantify how emotions are channelled through specific associations in texts, e.g., how did customers frame their ideas and emotions towards "beds" in hotel reviews? We release EmoAtlas as a standalone computational tool and discuss its impact in modelling texts as data, promisingly filling current research gaps in AI and cognitive science.

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Section: Text Analysis In Emoatlasmentioning

confidence: 99%

EmoAtlas: An emotional profiling tool merging psychological lexicons, artificial intelligence and network science

Semeraro

Vilella

Mohammad³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to calculate the influence of word frequency on vocabulary difficulty, this paper uses the Gutenberg plan, which counts the words of all electronic publications with a large vocabulary and is more persuasive for the provided word frequency, as the source of word frequency [17]. Download and save the word frequencies of all words in the Gutenberg plan.…”

Section: Classification Of Vocabulary Difficulty Levelmentioning

confidence: 99%

Research on Mobile English Learning System Based on iOS

Zhen

2021

Security and Communication Networks

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In order to improve the use experience of mobile learning and reduce the response delay caused by the slow positioning of system resources, research on mobile English learning systems based on iOS is proposed. Firstly, the value of realizing mobile learning is objectively analyzed, and the practical significance of the research is clarified from the perspectives of social development and learning conditions. On this basis, taking the intelligent client equipped with an iOS operating system as the hardware condition of the system, the system architecture, including data access layer, business logic layer, and presentation layer, is constructed by MVC. In the model module, it is divided into ten difficulty levels by using the frequency of vocabulary, the length of words, and the number of syllables of a single word. The learning resources of the reading class are uniformly represented by the vector space model, and the improved TF-IDF is used to calculate the weight of eigenvalues in the resources. According to the calculation results, the resources are also divided into ten difficulty levels, and the divided resources correspond to their respective databases and use regular expressions to find the learning resources that match the access request. Finally, with the support of the iOS protocol, the feedback mechanism between each module is constructed by Express.js. By analyzing the access requests sent and received by the view, the timely call to the target database resources is realized to improve the response rate of the system. The test results show that the function of the designed system can be realized stably, and the module response delay time is stable within 1s; under the ideal network environment, the buffer delay is in the range of 0.4–0.6 s, and the loading delay is no more than 1.8 s when the network speed is 3 Mpbs, which has a good user experience.

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Text Study of Reader Magazine in the Context of Big Data

Zhang,

Xia

2023

Applied Mathematics and Nonlinear Sciences

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Exploring the textual emotional value of the Reader is to help readers understand the Reader’s embedded emotions in an all-around way. In this paper, two text analysis methods, latent semantic analysis and probabilistic latent semantic analysis are described, starting from the analysis model of text research, and the automatic text classification technique is illustrated. The principles of support vector machines are explained, the optimal decision function selection solution is performed using kernel functions, and a PLSA-SVM text analysis model is also constructed using the PLSA method jointly with SVM. The structural features of Reader magazine were analyzed and explained, and the PLSA-SVM text analysis model was used to analyze the data on the narrative features of Reader magazine. In terms of narrative themes, the main narrative themes of Reader magazine were distributed as affection and love, which increased by 16.84% and 17.42% from 2012 to 2021, respectively. In terms of narrative perspectives, the proportion of first-person and third-person narrative perspectives is comparable, increasing by 9.03% and 12.3% from 2012 to 2021, respectively. Thus, in the context of big data, the PLSA-SVM text analysis model can be used to analyze the text narrative features of Reader magazine effectively, and Reader magazine can use the analysis to optimize further the text content and issue texts that are attuned to readers’ emotions.

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Pull out all the stops: Textual analysis via punctuation sequences

Cited by 3 publications

References 30 publications

EmoAtlas: An emotional profiling tool merging psychological lexicons, artificial intelligence and network science

EmoAtlas: An emotional profiling tool merging psychological lexicons, artificial intelligence and network science

Research on Mobile English Learning System Based on iOS

Text Study of Reader Magazine in the Context of Big Data

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