Do translation universals exist at the syntactic-semantic level? A study using semantic role labeling and textual entailment analysis of English-Chinese translations Humanities and Social Sciences Communications

The emotions which have undergone the most variation from one period to the other are easily identified in the above graphs. Thus, the emotion that increased most in the Spanish pre-covid expansión to covid periods is sadness, followed by fear; that which decreases the most is trust. Coincidences in the greater or lesser expression of emotions in the two periodicals are notable since it provides evidence that the economic atmosphere is similar in the narratives of both periodicals in both periods.

Finally, a hybrid model was constructed by combining the three models and its performance was compared against the individual models. Deep learning-based models are more advanced than machine learning-based models in text classification. There are some limitations in using machine learning approaches which are dependency on the manual feature extraction and necessity of domain knowledge. By using deep learning, that is, neural approaches are able to embed machine learning models and map text into low-dimensional feature vectors without manual feature extraction (Minaee et al. 2021). The escalating prevalence of sexual harassment cases in Middle Eastern countries has emerged as a pressing concern for governments, policymakers, and human rights activists. In recent years, scholars have made significant strides in advancing our understanding of the typology and frequency of these cases through both empirical and theoretical contributions (Eltahawy, 2015; Ranganathan et al., 2021).

This tool helps you understand how these mentions evolve over time, enabling you to determine if your brand perception is improving. By analyzing these insights, you can make informed decisions to refine your strategies and improve your overall brand health. For example, with Sprout, you can pick your priority networks to monitor mentions all from Sprout’s Smart Inbox or Reviews feed. With Sprout, you can see the sentiment of messages and reviews to analyze trends faster. And for certain networks, you can use Listening to also track keywords related to your brand even when customers don’t tag you directly. That said, you also need to monitor online review forums and third-party sites.

Forecasting consumer confidence through semantic network analysis of online news

They also realized it was impossible for China to be transformed into a Western-style democracy when they were informed (mainly by the national news outlets, for example, The New York Times) of social unrest in Hong Kong and cyber censorship in China’s mainland. Consequently, the majority coalition of interest groups in the US increasingly embraced protectionism and nationalism as their guiding ideologies in tackling China, which was later strengthened by the “America First” policy when Donald J. Trump took office. In early 2018, the two biggest economies were embroiled in a full-blown trade dispute (Swenson and Woo, 2019). In 1979, when the two nations established a formal diplomatic relationship, they strengthened their diplomatic and economic ties (Kang, 2007; Kurlantzick, 2007). Despite a “constructive strategic partnership” sought by the Clinton administration, China was portrayed as an ideological and political “other” by The New York Times. Therefore, it is fair to conclude that the dominant ideologies pertaining to China in the US mainstream media have changed very little, if at all, over the past few decades.

False positive for this model is 26, while the False negative is 16, which gives a misclassification rate of 8.4% for the model, which showed a low misclassification rate. 14 shows that the number of ChatGPT App false-positive are higher than that of false negative. Overall, for the Amharic sentiment dataset, the CNN-Bi-LSTM model achieved 91.60%, 90.47%, 93.91% accuracy, precision, and recall, respectively.

• We have also evaluated the performance sensitivity of GML w.r.t the number of extracted semantic relations and the number of extracted KNN relations respectively.
• These steps are performed separately for sentiment analysis and offensive language identification.
• The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.
• While stemming and lemmatization are helpful in some natural language processing tasks, they are generally unnecessary in Transformer-based sentiment analysis, as the models are designed to handle variations in word forms and inflexions.

While these results verify the main contribution of the study there is still room for improvement. When working on this research problems like manually collecting and annotating the dataset is a very tiring task. Even though a promising accuracy was achieved the model was trained with limited dataset which made the model learn only limited features and only considered binary classification.

The degree to which cultures differ in their prevailing beliefs about one’s sense of control has important societal consequences including economic development71,72,73,74 and upward mobility75. 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. For example, a person may feel chronically disempowered in their daily lives but may feel empowered in the virtual world—whenever they address a large group of interested followers.

Data preprocessing

Mainly, user tweets/reviews belong to various genres such as hotel, restaurants and laptops. One of the pre-trained models is a sentiment analysis model trained on an IMDB dataset, and it’s simple to load and make predictions. While it is a useful pre-trained model, the data it is trained on might not generalize as well as other domains, such as Twitter. BERT (Bidirectional Encoder Representations from Transformers) is a top machine learning model used for NLP tasks, including sentiment analysis. Developed in 2018 by Google, the library was trained on English WIkipedia and BooksCorpus, and it proved to be one of the most accurate libraries for NLP tasks. Data mining is the process of using advanced algorithms to identify patterns and anomalies within large data sets.

Across both LibreTranslate and Google Translate frameworks, the proposed ensemble model consistently demonstrates the highest recall scores across all languages, ranging from 0.75 to 0.82. Notably, for Arabic, Chinese, and French, the recall scores are relatively higher compared to Italian. Similarly, GPT-3 paired with both LibreTranslate and Google Translate consistently shows competitive recall scores across all languages.

One of the evident issues arising from the analysis of this corpus is that the frequencies of emotions are similar in number to those in the Spanish corpus. Trust is again the most frequent, although it decreases in the second period (from 26.07 to 23.18%), while fear is the second most frequent emotion, although, by contrast, it increases in the second period, from 15.16 to 16.97%. Anticipation is also an important emotion in the context of our material, yet contrary to the Spanish corpus, it decreases slightly in the second period (16.54–16.21%), as does joy (9.78–9.33%). Less dominant emotions are surprise and disgust, which show almost no change between periods. Figures 14 and 15 show the changes in values when we compare the two periods in the Spanish and English periodicals, respectively. The columns in red represent decreasing trends taking place in the periods; the blue columns represent increasing trends.

When harvesting social media data, companies should observe what comparisons customers make between the new product or service and its competitors to measure feature-by-feature what makes it better than its peers. Companies should also monitor social media during product launch to see what kind of first impression the new offering is making. Social media sentiment is often more candid — and therefore more useful — than survey responses. The SentimentModel class helps to initialize the model and contains the predict_proba and batch_predict_proba methods for single and batch prediction respectively. In general, probabilistic regularities of human behavior do not fit in a single-context Kolmogorovian probability space19,20; their description requires multi-context probability measure supplemented by transition rules between different contexts.

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It can extract critical information from unstructured text, such as entities, keywords, sentiment, and categories, and identify relationships between concepts for deeper context. We chose spaCy for its speed, efficiency, and comprehensive built-in tools, which make it ideal for large-scale NLP tasks. Its straightforward API, support for over 75 languages, and integration with modern transformer models make it a popular choice among researchers and developers alike. As each dataset contains slightly different topics and keywords, it would be interesting to assess whether a combination of three different datasets could help to improve the prediction of our model. The positive, negative, and neutral scores are ratios for the proportions of text that fall in each category and should sum to 1.

It can be observed that \(t_2\) has three relational factors, two of which are correctly predicted while the remaining one is mispredicted. However, GML still correctly predicts the label of \(t_2\) because the majority of its relational counterparts indicate a positive polarity. It is noteworthy that GML labels these examples in the order of \(t_1\), \(t_2\), \(t_3\) and \(t_4\).

In the dual architecture, feature detection layers are composed of three convolutional layers and three max-pooling layers arranged alternately, followed by three LSTM, GRU, Bi-LSTM, or Bi-GRU layers. Finally, the hybrid layers are mounted between the embedding and the discrimination layers, as described in Figs. Binary representation is an approach used to represent text documents by vectors of a length equal to the vocabulary size. Documents are quantized by One-hot encoding to generate the encoding vectors30. The representation does not preserve word meaning or order, so similar words cannot be distinguished from entirely different worlds.

9, it can be found that after adding MIBE neologism recognition to the model in Fig. 7, the performance of each model is improved, especially the accuracy and F1 value of RoBERTa-FF-BiLSTM, RoBERTa-FF-LSTM, and RoBERTa-FF-RNN are increased by about 0.2%. Therefore, it is also demonstrated that there are a large number of non-standard and creative web-popular neologisms in danmaku text, which can negatively affect the model’s semantic comprehension and sentiment categorization ability semantic analysis of text if they are not recognized. Figure 4 illustrates the matrices corresponding to the syntactic features utilized by the model. The Part-of-Speech Combinations and Dependency Relations matrices reveal the frequency and types of grammatical constructs present in a sample sentence. Similarly, the Tree-based Distances and Relative Position Distance matrices display numerical representations of word proximities and their respective hierarchical connections within the same sentence.

This facilitates a more accurate determination of the overall sentiment expressed. Machine learning tasks are domain-specific and models are unable to generalize their learning. This causes problems as real-world data is mostly unstructured, unlike training datasets. However, many language models are able to share much of their training data using transfer learning to optimize the general process of deep learning. The application of transfer learning in natural language processing significantly reduces the time and cost to train new NLP models. There are different text types, in which people express their mood, such as social media messages on social media platforms, transcripts of interviews and clinical notes including the description of patients’ mental states.

Given that the two periodicals under investigation here are both very prominent in their respective spheres of influence, it seems probable that their dissemination would have had consequences in terms of the behaviour of investors in general. It can be concluded that H2 is supported by the previous analysis of both newspapers, as they both reflect a shift in focus towards the impact of the global health crisis on different aspects of the economy and society. Figures 12 (expansión) and 13 (economist) show the occurrence of the eight emotions in each corpus for each period. With the word limit imposed by EmoLex, the result of the automatic search function is a list of unigrams by frequency with the polarity and emotions marked, as shown in Fig.

What this article covers

These models are pre-trained on large amounts of text data, including social media content, which allows them to capture the nuances and complexities of language used in social media35. Another advantage of using these models is their ability to handle different languages and dialects. The models are trained on multilingual data, which makes them suitable for analyzing sentiment in text written in various languages35,36.

These models leverage subword embeddings, attention mechanisms and transformers to effectively handle higher dimension embeddings. GloVe is computationally efficient compared to some other methods, as it relies on global statistics and employs matrix factorization techniques to learn the word vectors. The model can be trained on large corpora without the need for extensive computational resources.

They can facilitate the automation of the analysis without requiring too much context information and deep meaning. Additionally, semantic role labelling focuses on extracting the information structure of a sentence while textual entailment estimates the informational explicitness of a text. While existing literature lays a solid groundwork for Aspect-Based Sentiment Analysis, our model addresses critical limitations by advancing detection and classification capabilities in complex linguistic contexts. Our Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) integrates a biaffine attention mechanism and a sophisticated graph-based approach to enhance nuanced text interpretation.

A key feature of the tool is entity-level sentiment analysis, which determines the sentiment behind each individual entity discussed in a single news piece. Focusing specifically on social media platforms, these tools are designed to analyze sentiment expressed in tweets, posts and comments. They help businesses better understand their social media presence and how their audience feels about their brand. It supports over 30 languages and dialects, and can dig deep into surveys and reviews to find the sentiment, intent, effort and emotion behind the words. Sprout Social offers all-in-one social media management solutions, including AI-powered listening and granular sentiment analysis.

Both MR and SST are movie review collections, CR contains the customer reviews of electronic products, while Twitter2013 contains microblog comments, which are usually shorter than movie and product reviews. It is noteworthy that all the above-mentioned deep learning solutions for SLSA were built upon the i.i.d learning paradigm. For a down-stream task of SLSA, their practical efficacy usually depends on sufficiently large quantities of labeled training data.

The startup’s solution finds applications in challenging customer service areas such as insurance claims, debt recovery, and more. Below, you get to meet 18 out of these promising startups & scaleups as well as the solutions they develop. These natural language processing startups are hand-picked based on criteria such as founding year, location, funding raised, & more.

You can also monitor review sites such as Google Reviews, Yelp and TripAdvisor, and online communities and forums like Reddit and Quora. Now that we’ve covered sentiment analysis and its benefits, let’s dive into the practical side of things. This section will guide you through four steps to conduct a thorough social sentiment analysis, helping you transform raw data into actionable strategies. As you look at how users interact with your brand and the types of content they prefer, you can retool your brand messaging for greater impact.

Sentiment Analysis

For each user in the sample, we calculated the average use of passive voice and average Twitter followers (a user may gain followers over the course of the sampling duration); the number of followers was rounded to the nearest integer. Much of the previous work concerning the relation between linguistic and personal agency relied on qualitative discourse analyses. For example, a qualitative report suggests that individuals dealing with chronic pain often discuss their struggles using passive voice, supposedly reflecting a sense of reduced personal agency23. Furthermore, qualitative descriptions of people’s reconstructions of psychological therapy show that patients describe periods of psychological hardship in a passive voice and that they often use more agentive language when describing the process of improvement24. The misclassification rate for CNN-BI-LSTM is calculated first by adding false positive and false negative, divided by the total testing dataset.

It includes many topic algorithms such as LDA, labeled LDA, and latent Dirichlet allocation (PLDA); besides, the input can be text in Excel or other spreadsheets. • We investigate select TM methods that are commonly used in text mining, namely, LDA, LSA, non-negative matrix factorization (NMF), principal component analysis (PCA), and random projection (RP). As there are many TM methods in the field of short-text data, and all definitely cannot be mentioned, we selected the most significant methods for our work. • We review scholarly articles related to TM from 2015 to 2020, including its common application areas, methods, and tools. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Emoji removal was deemed essential in sentiment analysis as it can convey emotional information that may interfere with the sentiment classification process. URL removal was also considered crucial as URLs do not provide relevant information and can take up significant feature space. The complete data cleaning and pre-processing steps are presented in Algorithm 1. The proportionate application of CDA and corpus linguistics helps analysts confirm, refute, or revise their own intuition by demonstrating why and to what extent their suspicions are founded (Partington, 2012, p. 12). With the advancement of CL and natural language processing (NLP) in recent years, new techniques have been applied to discourse studies, with sentiment analysis emerging as one of the most effective. The specific linguistic features described above capture the degree to which individuals represent a given state using agentive or non-agentive language.

The development of social media has led to the continuous emergence of new online terms in danmakus, and the sentiment lexicon is difficult to adapt to the diversity and variability of danmakus timely. Therefore, the effect of danmaku sentiment analysis methods based on sentiment lexicon isn’t satisfactory. A hybrid computational method that combines interpretative social analysis and computational techniques has emerged as a powerful approach in digital social research. This method enables the establishment of statistical strategies and facilitates quick prediction, particularly when dealing with large and complex datasets (Lindgren, 2020). To conduct a comprehensive study of social situations, it is crucial to consider the interplay between individuals and their environment. In this regard, emotional experience can serve as a valuable unit of measurement (Lvova et al., 2018).

In addition, bi-directional LSTM and GRU registered slightly more enhanced performance than the one-directional LSTM and GRU. Bag-Of-N-Grams (BONG) is a variant of BOW where the vocabulary is extended by appending a set of N consecutive words to the word set. The N-words ChatGPT sequences extracted from the corpus are employed as enriching features. But, the number of words selected for effectively representing a document is difficult to determine27. The main drawback of BONG is more sparsity and higher dimensionality compared to BOW29.

The LDA method can produce a set of topics that describe the entire corpus, which are individually understandable and also handle large-scale document–word corpus without the need to label any text. Initially, the topic model was used to define weights for the abstract topics. In this work, researchers compared extracted keywords from different techniques, namely, cosine similarity, word co-occurrence, and semantic distance techniques. They found that extracted keywords with word co-occurrence and semantic distance can provide more relevant keywords than the cosine similarity technique. As a result, testing of the model trained with a batch size of 128 and Adam optimizer was performed using training data, and we obtained a higher accuracy of 95.73% using CNN-Bi-LSTM with Word2vec to the other Deep Learning.

The amount of datasets in English dominates (81%), followed by datasets in Chinese (10%), Arabic (1.5%). When using non-English language datasets, the main difference lies in the pre-processing pipline, such as word segmentation, sentence splitting and other language-dependent text processing, while the methods and model architectures are language-agnostic. Reddit is also a popular social media platform for publishing posts and comments. The difference between Reddit and other data sources is that posts are grouped into different subreddits according to the topics (i.e., depression and suicide).

Whereas, a majority of the literature works in text mining/sentiment analysis seem to focus on predicting market prices or directional changes only few works looked into how financial news impacts stock market volatility. One of them is Kogan et al. (2009) which used Support Vector Machine (SVM) to predict the volatility of stock market returns. Their results indicate that text regression corelates well with current and historical volatility and a combined model performs even better. Similarly, Hautsch and Groß-Klußmann (2011) found that the release of highly relevant news induces an increase in return volatility, with negative news having a greater impact than positive news. Sentiment analysis is the larger practice of understanding the emotions and opinions expressed in text. Semantic analysis is the technical process of deriving meaning from bodies of text.

On the computational complexity of scalable gradual inference, the analytical results on SLSA are essentially the same as the results represented in our previous work on ALSA6. The sentiment tool includes various programs to support it, and the model can be used to analyze text by adding “sentiment” to the list of annotators. Read our in-depth guide to the top sentiment analysis solutions, consider feedback from active users and industry experts, and test the software through free trials or demos to find the best tool for your business.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Matrices depicting the syntactic features leveraged by the framework for analyzing word pair relationships in a sentence, illustrating part-of-speech combinations, dependency relations, tree-based distances, and relative positions. The overall architecture fine-grained sentiments comprehensive model for aspect-based analysis. The model using Logistic regression (LR) outperformed compared to the other five algorithms, where the accuracy is 75.8%.

Similarly, in an Urdu sentence, the order of words can be changed but the sense/meaning stays the same, as in “Meeithay aam hain” and “Aam meeithay hain,” both of which have the same meaning “Mangos are sweet”. Manual annotation of user reviews also one of the reasons for miss classification. Similarly, in work44, the comparison of NB versus SVM for the language preprocessing steps of Urdu documents reveals that SVM performs better than NB regarding accuracy. Additionally, normalized term frequency gives much improved results for feature selection.