scholarly journals Automatic classification of the emotional content of web documents

2021 ◽  
Author(s):  
Alaa Hussainalsaid
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Automatic Classification ◽  
Emotional Content ◽  
Web Pages ◽  
Web Documents ◽  
N Gram

This thesis proposes automatic classification of the emotional content of web documents using Natural Language Processing (NLP) algorithms. We used online articles and general documents to verify the performance of the algorithm, such as general web pages and news articles. The experiments used sentiment analysis that extracts sentiment of web documents. We used unigram and bigram approaches that are known as special types of N-gram, where N=1 and N=2, respectively. The unigram model analyses the probability to hit each word in the corpus independently; however, the bigram model analyses the probability of a word occurring depending on the previous word. Our results show that the unigram model has a better performance compared to the bigram model in terms of automatic classification of the emotional content of web documents.

scholarly journals Automatic classification of the emotional content of web documents

2021 ◽  
Author(s):  
Alaa Hussainalsaid
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Automatic Classification ◽  
Emotional Content ◽  
Web Pages ◽  
Web Documents ◽  
N Gram

This thesis proposes automatic classification of the emotional content of web documents using Natural Language Processing (NLP) algorithms. We used online articles and general documents to verify the performance of the algorithm, such as general web pages and news articles. The experiments used sentiment analysis that extracts sentiment of web documents. We used unigram and bigram approaches that are known as special types of N-gram, where N=1 and N=2, respectively. The unigram model analyses the probability to hit each word in the corpus independently; however, the bigram model analyses the probability of a word occurring depending on the previous word. Our results show that the unigram model has a better performance compared to the bigram model in terms of automatic classification of the emotional content of web documents.

scholarly journals Emotion Classification in Spanish: Exploring the Hard Classes

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 438
Author(s):  
Aiala Rosá ◽  
Luis Chiruzzo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Emotion Classification

The study of affective language has had numerous developments in the Natural Language Processing area in recent years, but the focus has been predominantly on Sentiment Analysis, an expression usually used to refer to the classification of texts according to their polarity or valence (positive vs. negative). The study of emotions, such as joy, sadness, anger, surprise, among others, has been much less developed and has fewer resources, both for English and for other languages, such as Spanish. In this paper, we present the most relevant existing resources for the study of emotions, mainly for Spanish; we describe some heuristics for the union of two existing corpora of Spanish tweets; and based on some experiments for classification of tweets according to seven categories (anger, disgust, fear, joy, sadness, surprise, and others) we analyze the most problematic classes.

Automatic Classification of Valve Diseases Through Natural Language Processing in Spanish and Active Learning

2021 ◽  
pp. 39-50
Author(s):  
Pablo Pérez-Sánchez ◽  
Víctor Vicente-Palacios ◽  
Manuel Barreiro-Pérez ◽  
Elena Díaz-Peláez ◽  
Antonio Sánchez-Puente ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Active Learning ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Classification ◽  
Valve Diseases

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.

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