Discursive use of stability in New York Times coverage of China: a sentiment analysis approach Humanities and Social Sciences Communications

Similarly, each confusion matrix provides insights into the strengths and weaknesses of different translator and sentiment analyzer model combinations in accurately classifying sentiment. Evaluating the numbers in these matrices helps understand the models’ overall performance and effectiveness in sentiment analysis tasks. The performance of the GPT-3 model is noteworthy, as it consistently demonstrated strong sentiment analysis capabilities when paired with either the LibreTranslate or Google Translate services. This finding underscores the versatility and robustness of the GPT-3 model for sentiment analysis tasks across different translation platforms.

All the raw materials have been manually cleaned to meet the needs of annotation and data analysis. Specifically, the current study first divides the sentences in each corpus into different semantic roles. You can foun additiona information about ai customer service and artificial intelligence and NLP. For each semantic role, a textual entailment analysis is then conducted to estimate and compare the average informational richness and explicitness in each corpus.

Setup

It is often argued that the use of agentive language is related to a sense of individual agency and control8,10. However, past research has not provided direct quantitative evidence for this claim. 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. When it comes to the relation between individuals’ own sense of personal agency and linguistic agency, previous studies have mostly utilized qualitative analyses27. In the current work, we present a comprehensive study of the relation between linguistic and personal agency, corroborating the longstanding suggestions of their supposed interrelatedness. To examine the relation between the number of followers and passive voice on social media, we first aggregated our twitter sample by users, to avoid dependencies in the model.

This paper adopts Maslow’s hierarchy of needs theory, which includes seven levels of physiological, safety, belonging and love, self-esteem, cognitive, aesthetic, and self-actualization needs, for guiding the labeling of danmaku emotions. This paper invited 10 senior Bilibili users to watch the video and then use the method to label the sentiment polarity of danmaku text. Compared with the labeling without using the method, the difficulty of the labeling is greatly reduced, and the speed and accuracy of the labeling are significantly improved. Comprehensive metrics and statistical breakdowns of these two datasets are thoughtfully compiled in a section of the paper designated as Table 2.

Methodology

In this post, we will compare and contrast the four NLP libraries mentioned above in terms of their performance on sentiment analysis for app reviews. It supports multimedia content by integrating with Speech-to-Text and Vision APIs to analyze audio files and scanned documents. They use News APIs to mine data and provide insights into how the media portrays a brand or topic. Classify sentiment in messages and posts as positive, negative or neutral, track changes in sentiment over time and view the overall sentiment score on your dashboard.

On the other hand, the last three sentences contain sexual words but do not convey any sexual harassment content. For example, the keyword ‘fear’ is used to describe death, ‘porn’ refers to a career contextually unrelated to explicit material, and ‘destroy’ pertains to damaging dishes. Therefore, manual interpretation plays a crucial role in accurately identifying sentences that truly contain sexual harassment content and avoiding any exceptions. Understanding customer sentiment on social media is an effective way to refine your brand strategy and improve customer engagement. By using the right sentiment analysis tools, you can gain valuable insights into how your audience feels about your brand and make informed decisions to enhance your online presence. Natural language processing tools use algorithms and linguistic rules to analyze and interpret human language.

A new index of importance for economic keywords

Word embeddings have proven invaluable for NLP tasks, as they allow machine learning algorithms to understand and process the semantic relationships between words in a more nuanced way compared to traditional methods. Sentiment analysis is the process of identifying and extracting opinions or emotions from text. It is a widely used technique in natural language processing (NLP) with applications in a variety of domains, including customer feedback analysis, social media monitoring, and market research.

Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data. Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. Semantic analysis ChatGPT App is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. This article explains the fundamentals of semantic analysis, how it works, examples, and the top five semantic analysis applications in 2022.

Aside from analyses of personal agency and agentive language in the context of psychopathology, research has also examined social power, which is another correlate of personal agency, related to agentive language. One notable example semantic analysis of text comes from Duranti27, who studied the local language of a western Samoan village, a language that can explicitly mark an agent in its grammar. He discovered that agentive language use corresponded with the speaker’s social position.

The predictors examined in our study, such as sense of power, number of followers, and depression, share the common characteristic of personal agency39,40,67,68,69. However, other factors and aspects related to these predictors may also contribute to the observed effects on linguistic agency. For instance, depression encompasses a wide range of symptoms and experiences beyond a lack of agency, and the number of followers may reflect various aspects of an individual’s online presence, such as social status or content quality. Similarly, the sense of power can be influenced by numerous personal and situational factors that may affect linguistic agency independently.

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

Literary texts and life writings offer unique perspectives on individual experiences and collective narratives related to this issue (Asl, 2023). However, analysing these sources poses significant challenges due to limitations in human cognitive processes. Extracting specific content from large-scale literary works requires meticulous attention to detail and an extensive amount of time. Researchers must carefully navigate through vast amounts of text to identify relevant passages that provide insights into sexual harassment experiences (Ennaji and Sadiqi, 2011). This process is further complicated by potential biases that may influence researchers’ interpretations or choices of which passages to include or exclude. Moreover, we measured the topic coherence score, and we observed that extracting fewer numbers of keywords led to a high coherence score in LDA and NMF TM methods.

By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates.

Translation universal hypothesis

An initial analysis with a million-word sample per sub-corpus was made with Lingmotif 2, for the reasons explained above. 5–10 correspond to the polarity and intensity of each sample from the pre-covid expansión, pre-covid economist, ChatGPT covid expansión and covid economist samples, respectively. As we can see, lexical items are rated as either positive/negative in terms of polarity (TSS) and as factual/slightly/fairly/very/extremely intense (TSI).

• In this study we used two-layer (Forward and Backward) Bi-LSTM, which obtain word embeddings from FastText.
• To do so, we built an LDA model to extract feature vectors from each day’s news and then deployed logistic regression to predict the direction of market volatility the next day.
• Deep learning techniques, inspired by the brain’s structural and autonomous learning ability, streamline computational model development and outperform standard machine learning in sentiment analysis, making them crucial for managing user-generated data19.
• As with the other forecasting models, we implemented an expanding window approach to generate our predictions.

The goal of SA is to identify the emotive direction of user evaluations automatically. The demand for sentiment analysis is growing as the need for evaluating and organizing hidden information in unstructured way of data grows. Offensive Language Identification (OLI) aims to control and minimize inappropriate content on social media using natural language processing. On media platforms, objectionable content and the number of users from many nations and cultures have increased rapidly. In addition, a considerable amount of controversial content is directed toward specific individuals and minority and ethnic communities.

• It opens up new possibilities for sentiment analysis applications in various fields, including marketing, politics, and social media analysis.
• Companies can deploy surveys to assess customer reactions and monitor questions or complaints that the service desk receives.
• If mental illness is detected at an early stage, it can be beneficial to overall disease progression and treatment.
• The challenge with inferring topics from short text is due to the fact that it contains relatively small amounts and noisy data that might result in inferring an inaccurate topic.

The proposed model achieves an accuracy of 91.18%, recall of 92.53%, F1-Score of 91.94%, and precision of 91.79%21. To mitigate this concern, incorporating cultural knowledge into the sentiment analysis process is imperative to enhance the accuracy of sentiment identification in translated text. Potential strategies include the utilization of domain-specific lexicons, training data curated for the specific cultural context, or applying machine learning models tailored to accommodate cultural differences. Integrating cultural awareness into sentiment analysis methodologies enables a more refined understanding of the sentiments expressed in the translated text, enabling comprehensive and accurate analysis across diverse linguistic and cultural domains. In this study, the selection of deep learning models was contingent on their suitability for Amharic sentiment analysis. During the model selection process criteria that is noted by Refs.22,23,24 were considered.

These are the class id for the class labels which will be used to train the model. This is how the data looks like now, where 1,2,3,4,5 stars are our class labels. Due to enormous number of uncontrolled degrees of freedom in the context (down to vacuum fluctuations of physical fields73, ch. 14), activation of the considered cog and the resulting cognitive-behavioral activity is fundamentally nondeterministic74. Corresponding probabilistic regularity is represented by potentiality state \(\left| \Psi \right\rangle\) as indicated in the Fig. Observable judgment or decision making records transition of a cognitive-behavioral system from state \(\left| \Psi \right\rangle\) to a new state corresponding to the option actualized.