CatSent: a Catalan sentiment analysis website

Balaguer, Pau; Teixidó, Ivan; Vilaplana, Jordi; Mateo, Jordi; Rius, Josep; Solsona, Francesc

doi:10.1007/s11042-019-07877-7

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Sentiment analysis refers to the computational study of subjective information (e.g., opinions, biases, attitudes), affective states, feelings, and emotions within text data using Natural Language Processing (NLP), text analysis, biometrics, and computational linguis-tics [31,65,66]. The methods of sentiment analysis can either be machine learning-based, lexicon-based, or hybrid [67] and they can be applied on an aspect, topic, document, or sentence level [68,69].…”

Section: Sentiment Analysismentioning

confidence: 99%

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

et al. 2022

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This study aims to understand the public’s perspectives, sentiments, attitudes, and discourses regarding the adoption, integration, and use of augmented reality and virtual reality in education and in general by analyzing social media data. Due to its nature, Twitter was the selected platform. Over 17 million tweets were retrieved from January 2010 to December 2020 and four datasets were created. Two of them referred to the general use of these technologies and two to their educational use. The data was analyzed using text mining, sentiment analysis (e.g., polarity and emotion detection), and topic modeling methods. TextBlob, Word-Emotion Association Lexicon (EmoLex), Valence Aware Dictionary for Sentiment Reasoning (VADER), and Latent Dirichlet Allocation (LDA) were some of the tools used. Based on the results, the majority of the public were positively disposed toward the general and the educational use of both augmented reality and virtual reality and mostly expressed positive emotions (e.g., anticipation, trust, and joy) when referring to them. In total, 11 topics emerged that were related to education, new technologies, digital and social media use, marketing and advertising, the industrial domain, the health domain, gaming, fitness and exercising, devices, the travel and tourism domain, and software development kits. The educational benefits of augmented reality and virtual reality, their ability to enrich both teaching and learning activities, and their role as effective educational means were evident.

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Section: Sentiment Analysismentioning

confidence: 99%

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

et al. 2022

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“…SA has been employed in different domains like product reviews [20], movie reviews [29], hotel reviews [30], news [31,32], consumers review [28], political debates [33], and social media posts (e.g., Facebook, Twitter, Weibo) [34,35]. One of the earliest works on sentimental analysis was conducted by [36], where the researchers performed sentiment analysis of movie reviews.…”

Section: Sentiment Analysismentioning

confidence: 99%

Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach

Bhagat

Mishra²,

Dixit

et al. 2021

Sustainability

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The aim of this study was to analyze public opinion about online learning during the COVID-19 (Coronavirus Disease 2019) pandemic. A total of 154 articles from online news and blogging websites related to online learning were extracted from Google and DuckDuckGo. The articles were extracted for 45 days, starting from the day the World Health Organization (WHO) declared COVID-19 a worldwide pandemic, 11 March 2020. For this research, we applied the dictionary-based approach of the lexicon-based method to perform sentiment analysis on the articles extracted through web scraping. We calculated the polarity and subjectivity scores of the extracted article using the TextBlob library. The results showed that over 90% of the articles are positive, and the remaining were mildly negative. In general, the blogs were more positive than the newspaper articles; however, the blogs were more opinionated compared to the news articles.

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“…Many accurate pre-trained models are already available for resource-rich languages (see Table 1, including for the sentiment analysis tasks); therefore, machine translation of less-resourced languages into rich-resourced has also been investigated. Improvements in statistical or neural machine translation systems eliminate the need to create separate monolingual Sentiment analysis models for separate languages [23][24][25][26][27][28][29][30][31][32]. For example, in [33], neural machine translation is used to convert the multilingual data set into English which is later classified as the English language model.…”

Section: Introductionmentioning

confidence: 99%

Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

2022

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We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset.

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CatSent: a Catalan sentiment analysis website

Cited by 5 publications

References 19 publications

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach

Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

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