Abstract:Social media platforms allow users to share thoughts, experiences, and beliefs. These platforms represent a rich resource for natural language processing techniques to make inferences in the context of cognitive psychology. Some inaccurate and biased thinking patterns are defined as cognitive distortions. Detecting these distortions helps users restructure how to perceive thoughts in a healthier way. This paper proposed a machine learning-based approach to improve cognitive distortions' classification of the A… Show more
“…Shoukry & Rafea, 2012), and tweet classification (E. Abozinadah, 2017;E. A. Abozinadah & Jones Jr, 2016;F. Alhaj et al, 2022;Brahimi et al, 2016;Mourad et al, 2017).…”
Stemming is an important task in natural language processing that involves reducing a word to its root form, or stem. In many cases, stemming can significantly improve the accuracy and efficiency of text analysis tasks such as information retrieval, text classification, and sentiment analysis. For the Arabic language, which has a rich morphology with a large number of prefixes and suffixes, stemming is particularly challenging. Tashaphyne provides an effective solution to this challenge, making it a valuable tool for researchers and practitioners working with Arabic text data.
“…Shoukry & Rafea, 2012), and tweet classification (E. Abozinadah, 2017;E. A. Abozinadah & Jones Jr, 2016;F. Alhaj et al, 2022;Brahimi et al, 2016;Mourad et al, 2017).…”
Stemming is an important task in natural language processing that involves reducing a word to its root form, or stem. In many cases, stemming can significantly improve the accuracy and efficiency of text analysis tasks such as information retrieval, text classification, and sentiment analysis. For the Arabic language, which has a rich morphology with a large number of prefixes and suffixes, stemming is particularly challenging. Tashaphyne provides an effective solution to this challenge, making it a valuable tool for researchers and practitioners working with Arabic text data.
“…This will help in mitigating the public's adverse reaction. Mitigation of this rejection will be better if there is a grouping of tweets based on topic, as in Research [36]. In this study, we used BERTaopic to classify the tweets.…”
The Criminal Code Bill, also known as Rancangan Kitab Undang-undang Hukum Pidana (RKUHP), passed in the House of Representatives (DPR) on December 6, 2022, is being debated because several issues need to be fixed. Therefore, research was conducted to determine the public's reaction to the ratification of the Criminal Code Bill by analyzing Twitter data. This study aims to obtain a general response to the legalized RKUHP. We use sentiment analysis, a text-processing method, to get data from the public. To do this, we used N-grams (unigrams, bigrams, and trigrams) along with three algorithms: Naïve Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM). The result of sentiment analysis found that 51% of tweets were positive about the ratification of the RKUHP, and 49% were negative. In addition, it was also found that SVM has the best accuracy compared to other algorithms, with an accuracy value of 0.81 on the unigram combination.
“…Study [16] introduced a supervised topic modeling approach, Hierarchical Dirichlet Process-based Inverse Regres-sion (HDP-IR), which is based on LDA and Inverse Regression, to extend to the approach's use in predictive models for variables such as customer sentiment, product quality, and affect. In study [17], BERTopic was used to extract topic probability distributions from topic modeling results from a Twitter dataset. These distributions were then combined with vector representations of the original data for use in a classification model, which demonstrated a better performance than a basic classification model.…”
The feedback shared by consumers on e-commerce platforms holds immense value in marketing, as it offers insights into their opinions and preferences, which are readily accessible. However, analyzing a large volume of reviews manually is impractical. Therefore, automating the extraction of essential insights from these data can provide more comprehensive and efficient information. This research focuses on leveraging clustering algorithms to automate the extraction of consumer intentions, related products, and the pros and cons of products from review data. To achieve this, a review dataset was created by performing web crawling on the Naver Shopping platform. The findings are expected to contribute to a more precise understanding of consumer sentiments, enabling marketers to make informed decisions across a wide range of products and services.
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