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

The proposed ensemble model is the most suitable option for sentiment analysis on these four languages, considering that different language-translator pairs may require different models for optimal performance. Sentiments are then aggregated to determine the overall sentiment of a brand, product, or campaign. In social science, the study of media bias has a long tradition dating back to the 1950s (White, 1950).

The number of topic clusters on your website will depend on the products or services your brand offers. The various articles (each targeting their own keyword cluster) all link back to a primary “pillar page,” that is focused on the larger topic of link building. Unlike keyword clusters, topic clusters are focused on more than just a single piece of content. By adding those terms, topics, or questions onto the page, you improve topical depth and thus practice semantic SEO.

In-Depth Analysis

By answering those questions in your web page content, not only do you improve your semantic signals, you also give your page the opportunity to rank at the top of the SERPs. Although content length is not an official ranking factor, longer content is more likely to display stronger semantic signals. By optimizing for these keyword groupings, you can improve the total number of keywords your content ranks for and build more meaning into your content. Keyword clusters are groups of similar keywords that share semantic relevance. Combined together, they are all centered on improving topical depth and better conveying the meaning of web content.

The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. You can foun additiona information about ai customer service and artificial intelligence and NLP. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification.

A semantic analysis-driven customer requirements mining method for product conceptual design

Some techniques that specialize in the study of media bias focus exclusively on one type of bias (Huang et al. 2021; Liu et al. 2021b; Zhang et al. 2017), thus not general enough. In particular, some studies on pre-trained word embedding models show that they have captured rich human knowledge and biases (Caliskan et al. 2017; Grand et al. 2022; Zeng what is semantic analysis et al. 2023). However, such works mainly focus on pre-trained models rather than media bias directly, which limits their applicability to media bias analysis. Finally, this section contains the baseline results generated using many deep learning algorithms such as CNN-1D, LSTM,GRU, Bi-GRU, Bi-LSTM and our proposed model based on mBERT model.

Even more critical appears the role of the MLEGCN and attention mechanisms, whose removal results in the most substantial decreases in F1 scores across nearly all tasks and both datasets. This substantial performance drop highlights their pivotal role in enhancing the model’s capacity to focus on and interpret intricate relational dynamics within the data. The attention mechanisms, in particular, are crucial for weighting the importance of different elements within the input data, suggesting that their ability to direct the model’s focus is essential for tasks requiring nuanced understanding and interpretation.

For example, the keyword cluster pictured in strategy #1 is a part of a larger topic cluster focused on link building. Topic clusters are groups of content pieces that are centered around a central topic. Content optimizer tools do the hard work of identifying all of the semantically-related terms for you. For example, when you use the products schema on a product page, you immediately convey to Google a variety of important details. Structured data makes clear the function, object, or description of the content.

For parsing and preparing the input sentences, we employ the Stanza tool, developed by Qi et al. (2020). Stanza is renowned for its robust parsing capabilities, which is critical for preparing the textual data for processing by our model. We ensure that the model parameters are saved based on the optimal performance observed in the development set, a practice aimed at maximizing the efficacy ChatGPT of the model in real-world applications93. Furthermore, to present a comprehensive and reliable analysis of our model’s performance, we average the results from five distinct runs, each initialized with a different random seed. This method provides a more holistic view of the model’s capabilities, accounting for variability and ensuring the robustness of the reported results.

Finally, dropouts are used as a regularization method at the softmax layer28,29. 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.

• Before I apply any other more complex models such as ANN, CNN, RNN etc, the performances with logistic regression will hopefully give me a good idea of which data sampling methods I should choose.
• The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance.
• The inconspicuousness of the World Wide Web (WWW) has permitted single user to engage in aggressive SNs speech data that has made text conversation7,8 or, more precisely, sentiment analysis (SA) is vital to understand the behaviors of people9,10,11,12,13,14,15.

This research underscores the significance of adopting a multi-class classification approach over the conventional binary positive–negative scheme. Because a multi-class framework offers a more nuanced and insightful breakdown of sentiments. Furthermore, the establishment of a standardized corpus emerges as a crucial endeavor. While this study’s primary focus revolves around political sentiment analysis, its applicability extends far beyond the political domain. The insights and methodologies developed herein can be readily extended to diverse sectors such as agriculture, industry, tourism, sports, entertainment, and areas concerning both employee and customer satisfaction. In the future research, a notably unexplored avenue pertains to the analysis of sarcastic comments in the Amharic language, presenting a promising area for further investigation.

All this having been said, I still hold a great appreciation for Wes Anderson’s work. The poster from my Criterion Collection DVD of Rushmore rests on the wall to the left of me as I write this article. There is a danger, however, to be found in the recently prevailing imbalance of semantic and syntactic for Wes Anderson’s work. While genre serves as a compelling comfort for audiences, it shouldn’t be something that defines a film. The evolution of Wes Anderson’s work shows a decreasing regard for dramaturgical stability in favor of feeding audiences the aesthetics and stylistic choices that Anderson churns out in his work.

Data preprocessing

This study delves into the realm of sentiment analysis in the Amharic language, focusing on political sentences extracted from social media platforms in Ethiopia. The research employs deep learning techniques, including Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN with Bi-LSTM to analyze and classify sentiments. The hybrid CNN-Bi-LSTM model emerges as the top performer, achieving an impressive accuracy of 91.60%.

Which sentiment analysis software is best for any particular organization depends on how the company will use it. Another business might be interested in combining this sentiment data to guide future product development, and would choose a different sentiment analysis tool. For instance, we may sarcastically use a word, which is often considered positive in the convention of communication, to express our negative opinion.

A hybrid dependency-based approach for Urdu sentiment analysis

The importance of customer sentiment extends to what positive or negative sentiment the customer expresses, not just directly to the organization, but to other customers as well. People commonly share their feelings about a brand’s products or services, whether they are positive or negative, on social media. If a customer likes or dislikes a product or service that a brand offers, they may post a comment about it — and those comments can add up. Such posts amount to a snapshot of customer experience that is, in many ways, more accurate than what a customer survey can obtain. Where Q, K, and V are abstract vectors that extract various components from an input word. The second stage is to replace 15% of tokens in each sentence with a [MASK] token (for example, the word ’Porana’ is substituted with a [MASK] token).

Therefore, we conducted different experiments using different deep-learning algorithms. Furthermore, dataset balancing occurs after preprocessing but before model training and evaluation41. 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.

And this time, instead of Regex, I used Spacy to parse the documents, and filtered numbers, URL, punctuation, etc. While trying to read the files into a Pandas dataframe, I found two files cannot be properly loaded as tsv file. It seems like there are some entries not properly tab-separated, so end up as a chunk of 10 or more tweets stuck together. I could have tried retrieving them with tweet ID provided, but I decided to first ignore these two files, and make up a training set with only 9 txt files. In this section, we look at how to load and perform predictions on the trained model. Again, semantic SEO encompasses a variety of strategies and concepts, but it all centers on meaning, language, and search intent.

Sentence-level sentiment analysis (SLSA) aims to analyze the opinions and emotions expressed in a sentence1 . Unlike aspect-level sentiment analysis (ALSA)2, which reasons about the local sentiment polarity expressed towards a specific aspect, SLSA needs to detect the general sentiment orientation of an entire sentence. In practice, SLSA is highly valuable in the scenarios where comments are represented by concise and isolated sentences with arbitrary topics, requiring a holistic analysis of sentiment at the sentence level. In another application, social media platforms (e.g., ChatGPT App Twitter and Facebook) usually analyze people’s comments and posts by SLSA to gain insights into public opinion and social trends. In conclusion, our model demonstrates excellent performance across various tasks in ABSA on the D1 dataset, suggesting its potential for comprehensive and nuanced sentiment analysis in natural language processing. However, the choice of the model for specific applications should be aligned with the unique requirements of the task, considering the inherent trade-offs in precision, recall, and the complexities of natural language understanding.

Compared to the other multilingual models, the proposed model’s performance gain may be due to the translation and cleaning of the sentences before the sentiment analysis task. In the fourth phase of the methodology, we conducted sentiment analysis on the translated data using pre-trained sentiment analysis deep learning models and the proposed ensemble model. In this study, we have utilized an ensemble of two pre-trained sentiment analysis models from Hugging Face33, namely, Twitter-Roberta-Base-Sentiment-Latest34, bert-base-multilingual-uncased-sentiment22 and the GPT-3 LLM from OpenAI23. The ensemble sentiment analysis model analyzed the text to determine the sentiment polarity (positive, negative, or neutral). The algorithm shows step by step process followed in the sentiment analysis phase.

To maintain output values between 0 and 1 for the binary classification task of negative and positive sentiment, a sigmoid activation function was applied. During training, the researcher measured accuracy, recall, and precision as performance metrics and conducted training over 10 epochs to optimize the model. The model is assessed on the test dataset once the model is fitted; the result is presented as shown below in Table 4. Word-embedding is a feature learning technique in which each word or phrase in the vocabulary is mapped to an N-dimensional real-number vector. The goal of word embedding is to convert all words in the dictionary into a lower-dimensional vector.

• This shift is evident in the increased coverage of health-related topics and the analysis of social concerns related to the pandemic.
• Deep learning and other transfer learning models help to analyze the presence of sentiment in texts.
• The PSS and NSS can then be calculated by a simple cosine similarity between the review vector and the positive and negative vectors, respectively.

The class labels 0 denotes positive, 1 denotes negative, 2 denotes mixed feelings, and 3 denotes an unknown state in sentiment analysis. Similarly, in offensive language identification, the class labels are 0 denotes not offensive, 1 denotes offensive untargeted, 2 denotes offensive targeted insult group, 3 denotes offensive target insult individual, and 4 denotes offensive target insult other. Figure 2 shows the training and validation set accuracy and loss values using Bi-LSTM model for sentiment analysis.