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

In the end, the GRU model converged to the solution faster with no large iterations to arrive at those optimal values. In summary, the GRU model for the Amharic sentiment dataset achieved 88.99%, 90.61%, 89.67% accuracy, precision, and recall, respectively. From Tables 4 and 5, it is observed that the proposed Bi-LSTM model for identifying sentiments and offensive language, performs better for Tamil-English dataset with higher accuracy of 62% and 73% respectively.

Yet the topics extracted from news sources can be used in predicting directional market volatility. It is interesting that topics alone contain a valuable information that can be used to predict the direction of market volatility. The evaluation of the classification model has demonstrated good prediction accuracy. It indicates that topics extracted from news could be used as a signal to predict the direction of market volatility the next day. Glasserman and Mamaysky (2019) used an N-gram model, which they applied to as many as 367,311 articles, to develop a methodology showing that unusual negative and positive news forecasts volatility at both the company-specific and aggregate levels. The authors find that an increase in the “unusualness” of news with negative sentiment predicts an increase in stock market volatility.

• By calculating the mutual information and eliminating the words with low branch entropy and removing the first and last deactivated words, the new word set is obtained after eliminating the existing old words.
• One issue that needs to be addressed in future studies is whether the associations observed in Studies 2 and 3 reflect a stable (trait-level) or state-level phenomenon.
• Previous work has mostly focused on examining how linguistic agency influences our attribution of personal agency to others, and specifically, how it affects attributions of blame19,65,66.
• Some researchers investigated how to integrate the traditional language features (e.g., part-of-speech, syntax dependency tree and knowledge-base) into pre-trained models for improved performance27,29,30.
• However, other factors and aspects related to these predictors may also contribute to the observed effects on linguistic agency.
• These models leverage subword embeddings, attention mechanisms and transformers to effectively handle higher dimension embeddings.

These tools help resolve customer problems in minimal time, thereby increasing customer satisfaction. All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform. The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle. The platform allows Uber to streamline and optimize the map data triggering the ticket. The semantic analysis uses two distinct techniques to obtain information from text or corpus of data. The first technique refers to text classification, while the second relates to text extractor.

Defining the negative and positive sets

It considers how frequently words co-occur with each other in the entire dataset rather than just in the local context of individual words. The Distributional Hypothesis posits that words with similar meanings tend to occur in similar contexts. This concept forms the basis for many word embedding models, as they aim to capture semantic relationships by analyzing patterns of word co-occurrence. A sliding context window is applied to the text, and for each target word, the surrounding words within the window are considered as context words. The word embedding model is trained to predict a target word based on its context words or vice versa.

Chinese-RoBerta-WWM-EXT, Chinese-BERT-WWM-EXT and XLNet are used as pre-trained models with dropout rate of 0.1, hidden size of 768, number of hidden layers of 12, max Length of 80. BiLSTM model is used for sentiment text classification with dropout rate of 0.5, hidden size of 64, batch size of 64, and epoch of 20. The model is trained using Adam optimizer with a learning rate of 1e−5 and weight decay of 0.01. The models utilized in this study were constructed using various algorithms, incorporating the optimal parameters for each algorithm. The evaluation of model performance was based on several metrics, including accuracy, precision, recall, and F1. Accuracy, precision, recall, and F1 are commonly employed to assess the performance of classification models.

• This study opens avenues for further research to enhance the accuracy and effectiveness of sentiment analysis models.
• RNNs process chronological sequence in both input and output, or only one of them.
• LSTM, a widely used architecture for RNNs, is capable of capturing long-term dependencies and influencing current predictions.
• Some instances of social media interaction include comments, likes, and shares that express people’s opinions.
• Sentiment analysis helps you gain insights into customer feedback, brand perception, or public opinion to improve on your business’s weaknesses and expand on its strengths.

Python’s NLP libraries aim to make text preprocessing as effortless as possible, so that applications can accurately convert free text sentences into a structured feature that can be used by a machine learning (ML) or deep learning (DL) pipeline. Combined with a user-friendly API, the latest algorithms and NLP models can be implemented quickly and easily, so that applications can continue to grow and improve. The standard CNN structure is composed of a convolutional layer and a pooling layer, followed by a fully-connected layer. Some studies122,123,124,125,126,127 utilized standard CNN to construct classification models, and combined other features such as LIWC, TF-IDF, BOW, and POS.

Evolving linguistic divergence on polarizing social media

Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches. The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance. Subword embeddings, such as FastText, represent words as combinations of subword units, providing more flexibility and handling rare or out-of-vocabulary words. The objective function is optimized using gradient descent or other optimization algorithms. The goal is to adjust the word vectors and biases to minimize the squared difference between the predicted and actual logarithmic co-occurrence probabilities. Skip-gram works well with handling vast amounts of text data and is found to represent rare words well.

The outputs from the two LSTM layers are then merged using a variety of methods, including average, sum, multiplication, and concatenation. Bi-LSTM trains two separate LSTMs in different directions (one for forward and the other for backward) on the input pattern, then merges the results28,31. Once the learning model has been developed using the training data, it must be tested with previously unknown data. This data is known as test data, and it is used to assess the effectiveness of the algorithm as well as to alter or optimize it for better outcomes. It is the subset of training dataset that is used to evaluate a final model accurately. The test dataset is used after determining the bias value and weight of the model.

What is the main goal of sentiment analysis?

The difficulty of capturing semantics and concepts of the language from words proposes challenges to the text processing tasks. A document can not be processed in its raw format, and hence it has to be transformed into a machine-understandable representation27. Selecting the convenient representation scheme suits the application is a substantial step28. The fundamental methodologies used to represent text data as vectors are Vector Space Model (VSM) and neural network-based representation. Text components are represented by numerical vectors which may represent a character, word, paragraph, or the whole document.

Over time, scientists developed numerous complex methods to understand the relations in the text datasets, including text network analysis. In conclusion, drawing on the approaches of CDA and sentiment analysis, this study closely examines the use of non-quotation “stability” in relation to China by The New York Times between 1980 and 2020. However, the US government was vigilant about China’s growing economic and military power, not to mention its political presence in the world. 1, Period 1 is the only period in which the sentiment value semantic analysis of text is positive (0.09), which may be explained by the relatively harmonious China-US relations that began in 1979 when the two nations agreed to recognize each other. In Period 2, the sentiment value (−0.19) fell to an all-time low, as social problems and human rights issues began to garner a great deal of attention after the 1989 “Tiananmen Square incident”. The sentiment value for Period 3 (−0.06) is a bit greater than that of the previous period (−0.04), although it is still negative, and the score for Period 4 (−0.02) also increases.

Step 3. Use the best social media sentiment analysis tool

The keywords of each sets were combined using Boolean operator “OR”, and the four sets were combined using Boolean operator “AND”. In Extract (1), the newspaper emphasizes the significance of relations between its native country (the United States) and two East Asian nations (China and Japan) and describes the economic benefits of political stability in East Asia (an oil boom). From the perspective of The New York Times, the US, as an ally of Japan and a third party involved in the Sino-Japan Diaoyu Islands dispute (Wang, 2017), was an ChatGPT outsider regarding oil drilling in the East China Sea and foreign affairs relating to Japan. The implicit nomination strategy employed by The New York Times here legitimates the US’s appearance in this utterance. It is clear that this perspective is largely US-centric given the fact that the US is not an East Asian country. The data for raw frequency and standardized frequency are presented in Table 2, where a measured increase in standardized frequency and, most notably, an abrupt increase from Period 2 to Period 3 can be observed.

While businesses should obviously monitor their mentions, sentiment analysis digs into the positive, negative and neutral emotions surrounding those mentions. Python is a high-level programming language that supports dynamic semantics, object-oriented programming, and interpreter functionality. Deep learning approaches for sentiment analysis are being tested in the Jupyter Notebook editor using Python programming. One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation.

The GRU-CNN model registered the second-highest accuracy value, 82.74, with nearly 1.2% boosted accuracy. Bi-LSTM, the bi-directional version of LSTM, was applied to detect sentiment polarity in47,48,49. A bi-directional LSTM is constructed of a forward LSTM layer and a backward LSTM layer.

Translation to base language: English

The embedded words were used as an input for bidirectional LSTM model and added a BI-LSTM layer using Keras. TensorFlow’s Keras now has a new bidirectional class that can be used to construct bidirectional-LSTM and then fit the model to our data. Experimental research design is a scientific method of investigation in which one or more independent variables are altered and applied to one or more dependent variables to determine their impact on the latter. In experimental research, experimental setup such as determining how many trials to run and which parameters, weights, methodologies, and datasets to employ. As someone who is used to working with English texts, I found it difficult in the first place to translate preprocessing steps routinely used for English texts to Arabic. Luckily, I later came across a Github repository with the code for cleaning texts in Arabic.

The gained results clearly show that all the machine learning classifiers perform better with word feature combination (1-2) and unigram. On the other hand, obtained results indicating that the set of machine learning algorithms performance is not satisfiable with trigram and bigram word feature. RF gain 55.00 % accuracy using trigram features had the lowest accuracy of all machine learning classifiers.

On the other hand, LSTMs are more sensitive to the nature and size of the manipulated data. Stacking multiple layers of CNN after the LSTM, GRU, Bi-GRU, and Bi-LSTM reduced the number of parameters and boosted the performance. In the Arabic language, the character form changes according to its location in the word.

Its dashboard has a clean interface, with a sidebar displaying filters for selecting the samples used for sentiment analysis. Next to the sidebar is a section for visualization where you can use colorful charts and reports for monitoring sentiments by topic or duration and summarize them in a keyword cloud. Furthermore, many details in the research process have much room for further improvement. Additional features, such as indices for contextual semantic characteristics and the number of argument structure nestifications, could be included in the analysis. Moreover, the current study does not involve the refinement of semantic analysis tools since the modification and improvement of language models require high technique level and a massive quantity of training materials.

Besides, its conversational AI uses predictive behavior analytics to track user intent and identifies specific personas. This enables businesses to better understand their customers and personalize product or service offerings. In my testing, longer prompts can result in ChatGPT losing the request and, instead, offering a summary or analysis. Genism is a bespoke Python library that has been designed to deliver document indexing, topic modeling and retrieval solutions, using a large number of Corpora resources. This means it can process an input that exceeds the available RAM on a system.

Its ability to quickly identify patterns and trends related to various phenomena makes it particularly well-suited for investigating issues such as sexual harassment. Social media sentiment analysis is the process of gathering and understanding customers’ perceptions of a product, service or brand. The analysis uses advanced algorithms and natural language processing (NLP) to evaluate the emotions behind social media interactions. TM methods have been established for text mining as it is hard ChatGPT App to identify topics manually, which is not efficient or scalable due to the immense size of data. Various TM methods can automatically extract topics from short texts (Cheng et al., 2014) and standard long-text data (Xie and Xing, 2013). Such methods provide reliable results in numerous text analysis domains, such as probabilistic latent semantic analysis (PLSA) (Hofmann, 1999), latent semantic analysis (LSA) (Deerwester et al., 1990), and latent Dirichlet allocation (LDA) (Blei et al., 2003).

We will be leveraging both nltk and spacy which usually use the Penn Treebank notation for POS tagging. You can foun additiona information about ai customer service and artificial intelligence and NLP. Parts of speech (POS) are specific lexical categories to which words are assigned, based on their syntactic context and role. We will first combine the news headline and the news article text together to form a document for each piece of news. Thus, we can see the specific HTML tags which contain the textual content of each news article in the landing page mentioned above.

This helps you stay informed about trending topics, competitors and complementary products. By analyzing the sentiment behind user interactions, you can fine-tune your messaging strategy to better align with your audience’s values and preferences. This can lead to more effective marketing campaigns and a stronger brand presence.

This suggests that while the refinement process significantly enhances the model’s accuracy, its contribution is subtle, enhancing the final stages of the model’s predictions by refining and fine-tuning the representations. A deep learning model is built where the architecture is like the sentiment classification, which is an LSTM-GRU model shown in Fig. Instead of a 3 neurons-dense layer as the output layer, a 5 neurons-dense layer to classify the 5 emotions. The model had been trained using 20 epochs, and the history of the accuracy and loss had been plotted and shown in Fig.