scholarly journals Using Ensemble Models to Classify the Sentiment Expressed in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8931 ◽  
Author(s):  
James A. McCart ◽  
Dezon K. Finch ◽  
Jay Jarman ◽  
Edward Hickling ◽  
Jason D. Lind ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Regular Expression ◽  
Shared Task ◽  
Suicide Notes ◽  
The Mean ◽  
The U.S

In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F1 score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875).

scholarly journals Combining Lexico-semantic Features for Emotion Classification in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8960 ◽  
Author(s):  
Bart Desmet ◽  
Véronique Hoste
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Bag Of Words ◽  
Semantic Features ◽  
Emotion Classification ◽  
Shared Task ◽  
Suicide Notes ◽  
Sentence Level ◽  
Semantic Resources

This paper describes a system for automatic emotion classification, developed for the 2011 i2b2 Natural Language Processing Challenge, Track 2. The objective of the shared task was to label suicide notes with 15 relevant emotions on the sentence level. Our system uses 15 SVM models (one for each emotion) using the combination of features that was found to perform best on a given emotion. Features included lemmas and trigram bag of words, and information from semantic resources such as WordNet, SentiWordNet and subjectivity clues. The best-performing system labeled 7 of the 15 emotions and achieved an F-score of 53.31% on the test data.

scholarly journals A Hybrid Model for Automatic Emotion Recognition in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8948 ◽  
Author(s):  
Hui Yang ◽  
Alistair Willis ◽  
Anne De Roeck ◽  
Bashar Nuseibeh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Emotion Recognition ◽  
Hybrid Model ◽  
Language Processing ◽  
Automated System ◽  
Keyword Spotting ◽  
Emotion Classification ◽  
Shared Task ◽  
Suicide Notes

We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Coarse-Grained Sentiment Analysis Berbasis Natural Language Processing – Ulasan Hotel

2021 ◽  
Vol 10 (1) ◽  
pp. 41-48
Author(s):  
Warnia Nengsih ◽  
M. Mahrus Zein ◽  
Nazifa Hayati
Keyword(s):  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Receiver Operating Characteristic ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Operating Characteristic ◽  
Coarse Grained ◽  
Receiver Operating

Sentiment analysis adalah metode untuk memperoleh data dari berbagai platform yang tersedia di internet. Kemajuan teknologi memungkinkan mesin untuk mengenali suatu istilah yang dianggap sebagai opini positif maupun sebaliknya. Data-data dan opini tersebut berperan penting sebagai umpan balik produk, layanan, dan topik lainnya. Tanpa perlu memperoleh opini secara langsung dari masyarakat, pihak penyedia telah mendapatkan evaluasi yang penting guna mengembangkan diri. Bisnis perhotelan merupakan bidang yang terkait dengan jasa memberikan layanan pada pelanggan. Indikator keberlangsungan bisnis ini juga bergantung pada umpan balik pelanggannya dan dijadikan sebagai acuan untuk pengambilan kebijakan strategis. Teknik sentiment analysis berbasis Natural Language Processing dapat mengatasi permasalahan tersebut. Pada makalah ini prediksi dilakukan menggunakan classifier Random Forest (RF), sementara untuk merangkum kualitas classifier, digunakan kurva Receiver Operating Characteristic (ROC). Kurva ROC berupa grafik yang baik untuk merangkum kualitas classifier. Semakin tinggi kurva berada di atas garis diagonal, semakin baik prediksinya, dengan nilai kurva ROC yang diperoleh sebesar 0,90. Terlihat hasil ulasan terhadap opini pelanggan terhadap jasa dan pelayanan yang diberikan oleh hotel untuk kategori positif lebih banyak daripada kategori negatif. Polaritas dari ulasan diperoleh 68% ulasan pelanggan berada pada area positif dan 32% berada pada area negatif.

scholarly journals Sentiment Analysis on Twitter Airline Data

2021 ◽  
Vol 9 (VI) ◽  
pp. 3767-3770
Author(s):  
Kirti Jain
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Task ◽  
Model Based ◽  
Sentiment Mining ◽  
General Opinion

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

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