Sarcasm is an advanced linguistic expression often found on various online platforms. Sarcasm detection is challenging in natural language processing tasks that affect sentiment analysis. This article presents the inventive method of the semigraph, including semigraph construction and sarcasm detection processes. A variation of the semigraph is suggested in the pattern-relatedness of the text document. The proposed method is to obtain the sarcastic and non-sarcastic polarity scores of a document using a semigraph. The sarcastic polarity score represents the possibility that a document will become sarcastic. Sarcasm is detected based on the polarity scoring model. The performance of the proposed model enhances the existing prior art approach to sarcasm detection. In the Amazon product review, the model achieved the accuracy, recall, and f-measure of 0.87, 0.79, and 0.83, respectively.

Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.

This study investigates the impact of biased language, specifically 'Words that Wound,' on sentiment analysis in a dataset of 45,379 South Korean daily economic news articles. Using Word2Vec, cosine similarity, and an expanded lexicon, we analyzed the influence of these words on news titles' sentiment scores. Our findings reveal that incorporating biased language significantly amplifies sentiment scores' intensity, particularly negativity. The research examines the effect of heightened negativity in news titles on the KOSPI200 index using linear regression and sentiment analysis. Results indicate that the augmented sentiment lexicon (Sent1000), which includes the top 1,000 negative words with high cosine similarity to 'Crisis,' more effectively captures the impact of news sentiment on the stock market index than the original KNU sentiment lexicon (Sent0). The ARDL model and Impulse Response Function (IRF) analyses disclose that Sent1000 has a stronger and more persistent impact on KOSPI200 compared to Sent0. These findings emphasize the importance of understanding language's role in shaping market dynamics and investor sentiment, particularly the impact of negatively biased language on stock market indices. The study highlights the need for considering context and linguistic nuances when analyzing news content and its potential effects on public opinion and market dynamics.

The present paper is about the participation of our team "techno" on CERIST'22 shared tasks. We used an available dataset "task1.c" related to covid-19 pandemic. It comprises 4128 tweets for sentiment analysis task and 8661 tweets for fake news detection task. We used natural language processing tools with the combination of the most renowned pre-trained language models BERT (Bidirectional Encoder Representations from Transformers). The results shows the efficacy of pre-trained language models as we attained an accuracy of 0.93 for the sentiment analysis task and 0.90 for the fake news detection task.

Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free models remove the traditional tokenization step; however, their efficiency remains unclear. Moreover, the effect of tokenization is relatively unexplored in sequence tagging tasks. To this end, we investigate the impact of tokenization when extracting information from documents and present a comparative study and analysis of subword-based and character-based models. Specifically, we study Information Extraction (IE) from biomedical texts. The main outcome is twofold: tokenization patterns can introduce inductive bias that results in state-of-the-art performance, and the character-based models produce promising results; thus, transitioning to token-free IE models is feasible.

One of Europe's largest ammunition manufacturers has said efforts to meet surging demand from the war in Ukraine have been stymied by a new TikTok data centre that is monopolising electricity in the region close to its biggest factory. The chief executive of Nammo, which is co-owned by the Norwegian government, said a planned expansion of its largest factory in central Norway hit a roadblock due to a lack of surplus energy, with the construction of TikTok's new data centre using up electricity in the local area. "We are concerned because we see our future growth is challenged by the storage of cat videos," Morten Brandtzæg told the Financial Times. Demand for artillery rounds is 15 times higher than normal and Europe's munitions industry needs to invest €2bn in new factories to keep up with Ukraine's needs, according to Brandtzæg. By some estimates, Ukraine is firing 6,000 to 7,000 artillery shells a day and is facing ammunition shortages after more than a year of war.

This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemming, part-of-speech tagging, and related methods; practical applications in cross-language information retrieval systems, information extraction, and sentiment analysis; and methods and techniques used in Indonesian language NLP research, such as machine learning, statistics-based machine translation, and conflict-based approaches. This study also explores the application of NLP in Indonesian language industry and research and identifies challenges and opportunities in Indonesian language NLP research and development. Recommendations for future Indonesian language NLP research and development include developing more efficient methods and technologies, expanding NLP applications, increasing sustainability, further research into the potential of NLP, and promoting interdisciplinary collaboration. It is hoped that this review will help researchers, practitioners, and the government to understand the development of Indonesian language NLP and identify opportunities for further research and development. Designing an indonesian part of speech tagset and manually tagged indonesian corpus.

Considering a conversation thread, stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a given target. The target of the stance is expected to be an essential component in this task, being one of the main factors that make it different from sentiment analysis. However, a recent study shows that a target-oblivious model outperforms target-aware models, suggesting that targets are not useful when predicting stance. This paper re-examines this phenomenon for rumour stance classification (RSC) on social media, where a target is a rumour story implied by the source tweet in the conversation. We propose adversarial attacks in the test data, aiming to assess the models robustness and evaluate the role of the data in the models performance. Results show that state-of-the-art models, including approaches that use the entire conversation thread, overly relying on superficial signals. Our hypothesis is that the naturally high occurrence of target-independent direct replies in RSC (e.g. "this is fake" or just "fake") results in the impressive performance of target-oblivious models, highlighting the risk of target instances being treated as noise during training.

We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications. There are numerous applications of sparse features such as text generation and sentiment analysis. In this blog, we'll demonstrate how to perform sentiment analysis with the space features in BigQuery ML by training and inferencing machine learning models using a public dataset.

With the rise of big data, artificial intelligence (AI), and data mining techniques, group analysis has increasingly become a powerful tool in many applications, ranging from policy-making, direct marketing, education, to healthcare. For example, an important analysis strategy is group profiling, which extracts and describes the characteristics of groups of people [40]; it has been commonly used for customized recommendations to overcome sparse and missing personal data [25]. The same strategy is also used for mining social media, educational, and healthcare data to understand the shared characteristics of online communities or student/patient cohorts [15, 51, 100]. While it may help to support public and private services or product creations that are better tailored to different communities, group profiles resulted from mathematical inference are typically not valid for every individual regarded as a member in the group (this is known as non-distributive group profiles) [40]. The shared group characteristics extracted from data can have social ramifications such as stereotyping, stigmatization, or lead to pernicious consequences in decision making because individuals might be judged by group characteristics they do not posses [24, 56, 58].