You're likely familiar with the saying, "Texting is a brilliant way to miscommunicate how you feel and misinterpret what other people mean." You've probably even experienced it directly! Substitute "texting" with "email" or "online reviews" and you've struck the nerve of businesses worldwide. Gaining a proper understanding of what clients and consumers have to say about your product or service or, more importantly, how they feel about your brand, is a universal struggle for businesses everywhere. What if I told you it doesn't have to be this way?

Using a pre-trained language model (i.e. BERT) to apprehend source codes has attracted increasing attention in the natural language processing community. However, there are several challenges when it comes to applying these language models to solve programming language (PL) related problems directly, the significant one of which is the lack of domain knowledge issue that substantially deteriorates the model's performance. To this end, we propose the AstBERT model, a pre-trained language model aiming to better understand the PL using the abstract syntax tree (AST). Specifically, we collect a colossal amount of source codes (both java and python) from GitHub and incorporate the contextual code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated. We verify the performance of the proposed model on code information extraction and code search tasks, respectively. Experiment results show that our AstBERT model achieves state-of-the-art performance on both downstream tasks (with 96.4% for code information extraction task, and 57.12% for code search task).

Tourism is one of the most important economic sectors in the world (Hollenhorst The Bidirectional Encoder Representations et al., 2014), and its services have many from Transformers (BERT) is currently the characteristics that distinguish them from most important and state-of-the-art natural other products. Services are not tangible language model (Tenney et al., 2019) since and cannot be tested in advance, which is its launch in 2018 by Google. BERT Large, why the customer assumes an increased which is based on a Transformer risk before starting the trip. The service is architecture, is considered one of the most co-created together with the customer, so powerful language models with 24 layers, the customer is an active co-creator of the 16 attention heads, and 340 million service. Services are subject to the unoactu parameters (Lan et al. 2019). BERT is a principle, which means they are pretrained model and can be fine-tuned to produced at the same time as they are perform numerous downstream tasks such consumed, and they are considered as text classification, question answering, bilateral, i.e. a reciprocal relationship sentiment analysis, extractive between persons (Chehimi, 2014). In summarization, named entity recognition, addition, tourism services are relatively or sentence similarity (Egger, 2022). The expensive compared to everyday products model was pretrained on a huge English and have an intercultural dimension.

Electric Vehicles (EVs) are a rapidly growing component of the automotive industry and are projected to have over 30 percent of the overall United States light duty vehicle market by 2030 (Wolinetz and Axsen, 2017). It's very different from traditional researches realated to transportation about road conditions (Huang et al., 2019), aviation (Bauranov et al., 2021) and manned driving (Chai et al., 2021). Furthermore, the US and other countries have bet big on Battery Electric Vehicles (BEVs), allotting funding for charging infrastructure, subsidies and tax credits and setting deadlines to phase out combustion engine vehicles. Correspondingly, the stock price of EV companies like Tesla have recently far exceeded those of traditional auto manufacturers, helping to illustrate the bullish outlook many consumers and investors have toward EVs in general. Despite this, there remain concerns among both consumers and experts about various aspects of electric cars, and despite the excitement surrounding them, EV adoption rates hovered around 1.8% in 2020 (energy.gov,

The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To achieve this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04%. Further, since all the deep learning models show F1 scores above 75% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.

Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of events and their argument structures. % In this work, we propose a contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (participant) roles. To achieve this, we take advantage of text information extraction technologies to obtain event structural knowledge, and utilize multiple prompt functions to contrast difficult negative descriptions by manipulating event structures. We also design an event graph alignment loss based on optimal transport to capture event argument structures. In addition, we collect a large event-rich dataset (106,875 images) for pretraining, which provides a more challenging image retrieval benchmark to assess the understanding of complicated lengthy sentences. Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction on Multimedia Event Extraction, achieving more than 5\% absolute F-score gain in event extraction, as well as significant improvements on a variety of downstream tasks under zero-shot settings.

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multiview representations in a local-to-global manner. Extensive experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance.

Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare. Employing these technologies in the energy domain will be a significant contribution to the interdisciplinary topic of ``energy informatics", just like the related progress within the interdisciplinary area of ``bioinformatics". In this paper, we present the architecture of a Web-based semantic system called EneMonIE (Energy Monitoring through Information Extraction) for monitoring up-to-date energy trends through the use of automatic, continuous, and guided information extraction from diverse types of media available on the Web. The types of media handled by the system will include online news articles, social media texts, online news videos, and open-access scholarly papers and technical reports as well as various numeric energy data made publicly available by energy organizations. The system will utilize and contribute to the energy-related ontologies and its ultimate form will comprise components for (i) text categorization, (ii) named entity recognition, (iii) temporal expression extraction, (iv) event extraction, (v) social network construction, (vi) sentiment analysis, (vii) information fusion and summarization, (viii) media interlinking, and (ix) Web-based information retrieval and visualization. Wits its diverse data sources, automatic text processing capabilities, and presentation facilities open for public use; EneMonIE will be an important source of distilled and concise information for decision-makers including energy generation, transmission, and distribution system operators, energy research centres, related investors and entrepreneurs as well as for academicians, students, other individuals interested in the pace of energy events and technologies.

After transformer is proposed, lots of pre-trained language models have been come up with and sentiment analysis (SA) task has been improved. In this paper, we proposed a method that uses an auxiliary sentence about aspects that the sentence contains to help sentiment prediction. The first is aspect detection, which uses a multi-aspects detection model to predict all aspects that the sentence has. Combining the predicted aspects and the original sentence as Sentiment Analysis (SA) model's input. The second is to do out-of-domain aspect-based sentiment analysis(ABSA), train sentiment classification model with one kind of dataset and validate it with another kind of dataset. Finally, we created two baselines, they use no aspect and all aspects as sentiment classification model's input, respectively. Compare two baselines performance to our method, found that our method really makes sense.

We started doing sentiment analysis on NFL games by getting the overall average polarity of Tweets in Week 12. Last week, we really stepped it up and did an analysis on every game. This week, we're going to continue that trend.