Sentiment and emotion in financial journalism: a corpus-based, cross-linguistic analysis of the effects of COVID Humanities and Social Sciences Communications

By gradual learning, GML can effectively bridge distribution alignment between labeled training data and unlabeled target data. GML has been successfully applied to the task of Aspect-Level Sentiment Analysis (ALSA)6,7 as well as entity resolution8. Even without leveraging labeled training data, the existing unsupervised GML solutions can achieve competitive performance compared with supervised DNN models. However, the performance of these unsupervised solutions is still constrained by inaccurate and insufficient knowledge conveyance. For instance, the existing GML solution for aspect-level sentiment analysis mainly leverages sentiment lexicons and explicit polarity relations indicated by discourse structures to enable sentimental knowledge conveyance. On one hand, sentiment lexicons may be incomplete and a sentiment word’s actual polarity may vary in different sentence contexts; on the other hand, explicit polarity relations are usually sparse in natural language corpora.

The systematic refinement strategy further enhances its ability to align aspects with corresponding opinions, ensuring accurate sentiment analysis. Overall, this work sets a new standard in sentiment analysis, offering potential for various applications like market analysis and automated feedback systems. It paves the way for future research into combining linguistic insights with deep learning for more sophisticated language understanding.

In cases where access to training data is constrained, this research explores methods for translating sentiment lexica into the target language while simultaneously striving to enhance machine translation performance by generating additional contextual information. Sentiment analysis, a crucial natural language processing task, involves the automated detection of emotions expressed in text, distinguishing between positive, negative, or neutral sentiments. Nonetheless, conducting sentiment analysis in foreign languages, particularly without annotated data, presents complex challenges9. While traditional approaches have relied on multilingual pre-trained models for transfer learning, limited research has explored the possibility of leveraging translation to conduct sentiment analysis in foreign languages. Most studies have focused on applying transfer learning using multilingual pre-trained models, which have not yielded significant improvements in accuracy. However, the proposed method of translating foreign language text into English and subsequently analyzing the sentiment in the translated text remains relatively unexplored.

Experimenting using CNN

Short forms of words were expanded to full forms, stop words were removed, and synonyms were converted into normalized forms during preprocessing. The existing system with task, dataset language, and models applied and F1-score are explained in Table 1. For comparative evaluation, we use the benchmark datasets of movie review (MR), customer review (CR), Twitter2013 and Stanford Sentiment Treebank (SST).

These entities are known as named entities , which more specifically refer to terms that represent real-world objects like people, places, organizations, and so on, which are often denoted by proper names. A naive approach could be to find these by looking at the noun phrases in text documents. Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes. In dependency parsing, we try to use dependency-based grammars to analyze and infer both structure and semantic dependencies and relationships between tokens in a sentence. The basic principle behind a dependency grammar is that in any sentence in the language, all words except one, have some relationship or dependency on other words in the sentence.

It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software. Developed by a team of researchers at Google, including Tomas Mikolov, in 2013, Word2Vec (Word to Vector) has become a foundational technique for learning word embeddings in natural language processing (NLP) and machine learning models. Word embeddings, on the other hand, are dense vectors with continuous values that are trained using machine learning techniques, often based on neural networks. The idea is to learn representations that encode semantic meaning and relationships between words. Word embeddings are trained by exposing a model to a large amount of text data and adjusting the vector representations based on the context in which words appear. You can foun additiona information about ai customer service and artificial intelligence and NLP. NLTK’s sentiment analysis model is based on a machine learning classifier that is trained on a dataset of labeled app reviews.

Because BERT was trained on a large text corpus, it has a better ability to understand language and to learn variability in data patterns. For example, its dashboard displays data on a volume basis and the categorization of customer feedback on one screen. You can click on each category to see a breakdown of each issue that Idiomatic has detected for each customer, including billing, charge disputes, loan payments, and transferring credit. You can also export the data displayed in the dashboard by clicking the export button on the upper part of the dashboard. Pricing is based on NLU items, which measure API usage and are equivalent to one text unit, or up to 10,000 characters. There are altogether 4 argument structures nested in the English sentence, with each semantic role in the structure highlighted and labelled.

Validation is a sequence of instances used to fine-tune a classifier’s parameters. The texts are learned and validated for 50 iterations, and test data predictions are generated. These steps are performed separately for sentiment analysis and offensive language identification. The pretrained models like Logistic regression, CNN, BERT, RoBERTa, Bi-LSTM and Adapter-Bert are used text classification.

Deep learning-based danmaku sentiment analysis

We started by identifying the Economic Related Keywords (singletons or word sets). We then calculated the SBS indicators to measure the keyword’s importance and applied Granger causality methods to predict the consumer confidence indicators. Customer service platforms integrate with the customer relationship management (CRM) system. This integration enables a customer service agent to have the following information at their fingertips when the sentiment analysis tool flags an issue as high priority.

• All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform.
• Each and every word usually belongs to a specific lexical category in the case and forms the head word of different phrases.
• Furthermore, the integration of external syntactic knowledge into these models has shown to add another layer of understanding, enhancing the models’ performance and leading to a more sophisticated sentiment analysis process.
• Lastly, we considered a model based on BERT encodings65 as an additional forecasting baseline.
• Insights from social sentiment analytics can help you improve your brand recall and resonate better with your target audience.

Its extensive model hub provides access to thousands of community-contributed models, including those fine-tuned for specific use cases like sentiment analysis and question answering. Hugging Face also supports integration with the popular TensorFlow and PyTorch frameworks, bringing even more flexibility to building and deploying custom models. Hugging Face Transformers has established itself as a key player in the natural language processing field, offering an extensive library of pre-trained models that cater to a range of tasks, from text generation to question-answering. Built primarily for Python, the library simplifies working with state-of-the-art models like BERT, GPT-2, RoBERTa, and T5, among others.

The hyperparameters and the number of tests and training datasets used were the same for each model, even though the results obtained varied. Next, the experiments were accompanied by changing different hyperparameters until we obtained a better-performing model in support of previous works. During the experimentation, we used techniques like Early-stopping, and Dropout to prevent overfitting. The models used in this experiment were LSTM, GRU, Bi-LSTM, and CNN-Bi-LSTM with Word2vec, GloVe, and FastText. In this study, Keras was used to create, train, store, load, and perform all other necessary operations.

The platform is segmented into different packages and modules that are capable of both basic and advanced tasks, from the extraction of things like n-grams to much more complex functions. This makes it a great option for any NLP developer, regardless of their experience level. This open source Python NLP library has established itself as the go-to library for production usage, simplifying the development of applications that focus on processing significant volumes of text in a short space of time.

Finally, it is worth noting that the sentiment variable exhibits a significant correlation solely with the Personal component of the Consumer Confidence Index. We also tested different approaches, such as subtracting the median and dividing by the interquartile range, which did not yield better results. Contributing to this stream of research, we use a novel indicator of semantic importance to evaluate the possible impact of news on consumers’ confidence.

As a result, balancing the dataset in deep learning leads to improved model performance and reduced overfitting. Therefore, the datasets have up-sampled the positive and neutral classes and down-sampled the negative class via the SMOTE sampling technique. Large volumes of data can be analyzed by deep learning algorithms, which can identify intricate relationships and patterns that conventional machine learning methods might overlook20. The context of the YouTube comments, including the author’s location, demographics, and political affiliation, can also be analyzed using deep learning techniques. In this study, the researcher has successfully implemented a deep neural network with seven layers of movie review data.

Experimenting using GRU

This comprehensive integration of linguistic features is novel in the context of the ABSA task, particularly in the ASTE task, where such an approach has seldom been applied. Additionally, we implement a refining strategy that utilizes the outcomes of aspect and opinion extractions to enhance the representation of word pairs. This strategy allows for a more precise determination of whether word pairs correspond to aspect-opinion relationships within the context of the sentence. Overall, our model is adept at navigating all seven sub-tasks of ABSA, showcasing its versatility and depth in understanding and analyzing sentiment at a granular level.

• The process of concentrating on one task at a time generates significantly larger quality output more rapidly.
• 7 (performance statistics of mainstream baseline model for sentiment analysis), Fig.
• We will be leveraging a fair bit of nltk and spacy, both state-of-the-art libraries in NLP.
• Pricing is based on NLU items, which measure API usage and are equivalent to one text unit, or up to 10,000 characters.
• While trying to read the files into a Pandas dataframe, I found two files cannot be properly loaded as tsv file.
• Essentially, keyword extraction is the most fundamental task in several fields, such as information retrieval, text mining, and NLP applications, namely, topic detection and tracking (Kamalrudin et al., 2010).

To experiment, the researcher collected a Twitter dataset from the Kaggle repository26. Therefore, their versatility makes them suitable for various data types, such as time series, voice, text, financial, audio, video, and weather analysis. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context.

Lexicon based approaches use sentiment lexicons that contain words and their corresponding sentiment scores. The corresponding value identifies the word polarity (positive, negative, or neutral). These approaches do not use labelled datasets but require wide-coverage lexicons that include many sentiment holding words. Dictionaries semantic analysis of text are built by applying corpus-based or dictionary-based approaches6,26. The lexicon approaches are popularly used for Modern Standard Arabic (MSA) due to the lack of vernacular Arabic dictionaries6. Sentiment polarities of sentences and documents are calculated from the sentiment score of the constituent words/phrases.

For a simple solution, you should always look for a website builder that comes with features such as a drag-and-drop editor, and free SSL certificates. Text analysis applications need to utilize a range of technologies to provide an effective and user-friendly solution. Natural Language Processing (NLP) is one such technology and it is vital for creating applications that combine computer science, artificial intelligence (AI), and linguistics. However, for NLP algorithms to be implemented, there needs to be a compatible programming language used. One thing I’m not completely sure is that what kind of filtering it applies when all the data selected with n_neighbors_ver3 parameter is more than the minority class. As you will see below, after applying NearMiss-3, the dataset is perfectly balanced.

“Single-concept perception”, “Two-concept perception”, “Entanglement measure of semantic connection” sections describe a model of subjective text perception and semantic relation between the resulting cognitive entities. The frequency of economic and financial topics is consistently high in both periods in this The Economist, but there is a clear shift in focus in the 2020–2021 period due to the global health crisis. This shift is evident in the increased coverage of health-related topics and the analysis of social concerns related to the pandemic.

If a model achieved a high accuracy but is overfitted it won’t be useful in the real world because the model generalization capacity is not applicable. As shown in Table 10, 99.73%, 91.11% percent, and 91.60% percent accuracy were achieved for training, validation, and testing, respectively. This hybrid model outperforms previous models, and when looking at the marginal differences between training, validation, and testing, the difference is small, showing how well the model works in unknown datasets and its generalization ability. From the learning curve of the GRU model, the gap between the training and the validation accuracy is minimal, but the model at the start begins to underfit. However, when the researcher increases the epoch number, the accuracy increased, which overcomes underfitting. The loss was high with 64% at the first iteration, but it decreases to a minimum in the last epoch to 32%.

Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening. This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release. Upon parsing, the analysis then proceeds to the interpretation step, which is critical for artificial intelligence algorithms. For example, the word ‘Blackberry’ could refer to a fruit, a company, or its products, along with several other meanings. Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it. The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings.

It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites. GloVe embeddings are widely used in NLP tasks, such as text classification, sentiment analysis, machine translation and more. Unlike the Word2Vec models (CBOW and Skip-gram), which focus on predicting context words given a target word or vice versa, GloVe uses a different approach that involves ChatGPT optimizing word vectors based on their co-occurrence probabilities. The training process is designed to learn embeddings that effectively capture the semantic relationships between words. In machine translation systems, word embeddings help represent words in a language-agnostic way, allowing the model to better understand the semantic relationships between words in the source and target languages.

Therefore, the proposed approach can be potentially extended to handle other binary and even multi-label text classification tasks. Although existing researches have achieved certain results, they fail to completely solve the problems of low accuracy of danmaku text disambiguation, poor consistency of sentiment labeling, and insufficient semantic feature extraction18. This research presents a pioneering framework for ABSA, significantly advancing the field.

Post-factum fitting of phase data presented above is in line with the basic practice of quantum cognitive modeling14,15. In the present case, it constitutes finding of what the perception state should be in order to agree with the expert’s document ranking in the best possible way. Upgrading quantum decision model from descriptive to predictive status is possible by supplying it with quantum phase regularities encoding semantic stability of cognitive patterns144,145. Concurrence value (10) defines maximal violation of Bell’s inequality also used to detect entanglement of two-qubit state (4) in quantum physics and informatics87,111. This relates the model of perception semantics developed in this paper with Bell-based methods for quantification of quantum-like contextuality and semantics in cognition and behavior106,107,112,113.

Therefore, LSTM, BiLSTM, GRU, and a hybrid of CNN and BiLSTM were built by tuning the parameters of the classifier. From this, we obtained an accuracy of 94.74% using LSTM, 95.33% using BiLSTM, 90.76% using GRU, and 95.73% using ChatGPT App the hybrid of CNN and BiLSTM. Generally, the results of this paper show that the hybrid of bidirectional RNN(BiLSTM) and CNN has achieved better accuracy than the corresponding simple RNN and bidirectional algorithms.