Unifying aspect-based sentiment analysis BERT and multi-layered graph convolutional networks for comprehensive sentiment dissection Scientific Reports

The exclusion of syntactic features leads to varied impacts on performance, with more significant declines noted in tasks that likely require a deeper understanding of linguistic structures, such as AESC, AOPE, and ASTE. This indicates that syntactic features are integral to the model’s ability to parse complex syntactic relationships effectively. 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.

• In future, to increase system performance multitask learning can be used to identify sentiment analysis and offensive language identification.
• Overall, this study offers valuable insights into the potential of semantic network analysis in economic research and underscores the need for a multidimensional approach to economic analysis.
• Conceived the study, conducted the majority of the experiments, and wrote the main manuscript text.
• BERT (Bidirectional Encoder Representations from Transformers) is a top machine learning model used for NLP tasks, including sentiment analysis.

This work was supported by the Humanities and Social Sciences Planning Fund of Ministry of Education, China (Grant No. 22YJAZH039). For semantic adjuncts, the results show that the p-values of the comparison between the ANPS of adverbials (ADV) and manners (MNR) are smaller than 0.05. However, the effect sizes of the two U tests are not big enough (relatively 0.083 and 0.086) to ChatGPT App support significant differences. On the other hand, ANPS of discourse markers (DIS) in CT is significantly higher than that in CO with a relatively larger effect size (0.241), indicating a higher frequency of discourse markers in CT. The value range of Lin Similarity is divided into 9 subintervals, and the number of texts in CT and CO that fall into each subinterval is counted.

Cdiscount’s semantic analysis of customer reviews

In the same vein, Damásio (2018) and TenHouten (2014) also refute the existence of the reason–emotion duality, arguing that emotions are fundamental in decision-making and goal-formation. Not surprisingly, “greed and fear are two concepts widely used in experimental financial economics” (Barone-Adesi et al., 2018, p. 46) and constitute two divergent emotional states that underlie market uncertainties and volatilities. Such events have an impact on the language used in news journalism, and linguists can seek to identify certain patterns here. Interestingly Trump features in both the most positive and the most negative world news articles.

For instance, the discernible clusters in the POS embeddings suggest that the model has learned distinct representations for different grammatical categories, which is crucial for tasks reliant on POS tagging. Moreover, the spread and arrangement of points in the dependency embeddings indicate the model’s ability to capture a variety of syntactic dependencies, a key aspect for parsing and related NLP tasks. Such qualitative observations complement our quantitative findings, together forming a comprehensive evaluation of the model’s performance. Our experimental evaluation on the D1 dataset presented in Table 4 included a variety of models handling tasks such as OTE, AESC, AOP, and ASTE. These models were assessed on their precision, recall, and F1-score metrics, providing a comprehensive view of their performance in Aspect Based Sentiment Analysis.

Types of sentiment analysis

Its pre-trained models can perform various NLP tasks out of the box, including tokenization, part-of-speech tagging, and dependency parsing. Its ease of use and streamlined API make it a popular choice among developers and researchers working on NLP projects. We picked Hugging Face Transformers for its extensive library of pre-trained models and its flexibility in customization. Its user-friendly interface and support for multiple deep learning frameworks make it ideal for developers looking to implement robust NLP models quickly. The feature vector for an interval is a topic-count sparse vector, it represents the number of times each topic appears in headlines/tweets or articles within the given interval. The target vector is then constructed by pairing binary direction labels from market volatility data to each feature vector.

Accuracy serves as a measure of the proportion of correct predictions out of the total predictions made by the model. Precision and recall provide more nuanced evaluations of classification models. Precision represents the ratio of true positive predictions to all predicted positive instances, while recall denotes the ratio of true positive predictions to all actual positive instances.

• It represents an enhanced and corrected version of an earlier dataset put forth by Peng et al. in 2020, aiming to rectify previous inaccuracies79,90,91.
• Researchers can collect tweets using available Twitter application programming interfaces (API).
• To automatically measures whether individuals generate content reflecting a sense of personal agency, we relied on an approach termed Contextualized Construct Representation (CCR)55.
• Since the number of even single-word concepts in cognition of adult human is very large, each concept is passive most of the time, but may be activated by internal or external stimuli acquired e.g. from verbal or visual channels.
• The second-best performance was obtained by combining LDA2Vec embedding and implicit incongruity features.

These findings further underscore the complexity inherent in translation, highlighting its function as a dynamic balance system. In the feature fusion layer, the jieba thesaurus is first used to segment the text, for example, in the sentence “This is really Bengbu lived”, the jieba segmentation tool divides this sentence into [‘this’, ‘really’, ‘Bengbu’, ‘lived’, ‘had’]. In this paper, the number of words contained in each word in this sentence is counted to get the vector of [1,1,1,2,2]. When the word embedding vector output by RoBERTa is obtained, this paper averages the words in the same word and fills them into the original position, thus realizing the purpose of feature fusion, the logical structure is shown in Fig. The integration of syntactic structures into ABSA has significantly improved the precision of sentiment attribution to relevant aspects in complex sentences74,75. Syntax-aware models excel in handling sentences with multiple aspects, leveraging grammatical relationships to enhance sentiment discernment.

Table of contents

You can foun additiona information about ai customer service and artificial intelligence and NLP. MonkeyLearn is a cloud-based text mining platform that helps businesses analyze text and visualize data using machine learning. It offers seamless integrations with applications like Zapier, Zendesk, Salesforce, Google Sheets, and other business tools to automate workflows and analyze data at any scale. Through these robust integrations, users can sync help desk platforms, social media, and internal communication apps to ensure that sentiment data is always up-to-date.

The findings underscore the critical influence of translator and sentiment analyzer model choices on sentiment prediction accuracy. Additionally, the promising performance of the GPT-3 model and the Proposed Ensemble model highlights potential avenues for refining sentiment analysis techniques. Once a sentence’s translation is done, the sentence’s sentiment is analyzed, and output is provided. However, the sentences are initially translated to train the model, and then the sentiment analysis task is performed. Spanish startup AyGLOO creates an explainable AI solution that transforms complex AI models into easy-to-understand natural language rule sets.

Table 8 presents the baseline results achieved using a rule-based approach to validate our proposed UCSA-21 dataset. In this study, Urdu sentiment analysis text classification experiments have been performed to evaluate our proposed dataset by using a set of machine learning, rule-based and deep learning algorithms. As a baseline algorithm for better assessment, we performed tertiary classifications experiment with 9312 reviews from our suggested UCSA-21 dataset. We depict four evaluation measures applied for evaluations of a bunch of machine learning, rule-based, and deep learning algorithms such as accuracy, precision, recall, and F1-measure.

In the video below, hear examples of how you can use sentiment analysis to fuel business decisions and how to perform it. However, it’s important to remember that your customers are more than just data points. How they feel about you and your brand is an important factor in purchasing decisions, and analyzing this chatter can give you critical business insights. Yet, it’s easy to overlook audience emotions when you’re deep-diving into metrics because they’re difficult to quantify. • Stanford TMT, presented by Daniel et al. (2009), was implemented by the Stanford NLP group. It is designed to help social scientists or other researchers who wish to analyze voluminous textual material and tracking word usage.

Data extraction

To accurately discern sentiments within text containing slang or colloquial language, specific techniques designed to handle such linguistic features are indispensable. After that, this dataset is also trained and tested using an eXtended Language Model (XLM), XLM-T37. Which is a multilingual language model built upon the XLM-R architecture but with some modifications. Similar to XLM-R, it can be fine-tuned for sentiment analysis, particularly with datasets containing tweets due to its focus on informal language and social media data.

According to their findings, news are reflected in volatility more slowly at the aggregate than at the company-specific level, in agreement with the effect of diversification. The somehow-parallel approach by Caporin and Poli (2017) also found that news-related variables can improve volatility prediction. Certain news topics such earning announcements and upgrades/downgrades are more relevant than other news variables in predicting market volatility. Hugging Face is a company that offers an open-source software library and a platform for building and sharing models for natural language processing (NLP).

These findings suggest that real-life situations that involve a diminished sense of control and agency are strongly related to diminished linguistic agency. However, some may argue that our choice of control subreddits was not optimal because the depression subreddit predominantly prioritizes providing ChatGPT peer support, which may have its own unique linguistic structure. Therefore, we ran another replication (Study 3c) where we constructed a set of subreddits that are devoted to supporting and assisting others in topics unrelated to psychological help (e.g., cooking advice, programming support, etc.).

Using analytical tools, you can assess key metrics and themes pertinent to your brand. Tools like Sprout can help you automate this process, providing you with sentiment scores and detailed reports that highlight the overall mood of your audience. Monitoring these sentiments allows you to understand the overall perception of your brand. By understanding how your audience feels and reacts to your brand, you can improve customer engagement and direct interaction.

The negative recall or Specificity acheived 0.85 with the LSTM-CNN architecture. The negative precision or the true negative accuracy reported 0.84 with the Bi-GRU-CNN architecture. In some cases identifying the negative category is more significant than the postrive category, especially when there is a need to tackle the issues that negatively affected the opinion writer.

Each context word is represented as an embedding (vector) through a shared embedding layer. Prediction-based methods, particularly those like Word2Vec and GloVe (discussed below), have become dominant in the field of word embeddings due to their ability to capture rich semantic meaning and generalize well to various NLP tasks. Researchers, including Mnih and Hinton (2009), explored probabilistic models for learning distributed representations of words. These models focused on capturing semantic relationships between words and were an important step toward word embeddings. Pre-trained word embeddings serve as a foundation for pre-training more advanced language representation models, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). In text generation tasks, such as language modeling and autoencoders, word embeddings are often used to represent the input text and generate coherent and contextually relevant output sequences.

Performance evaluation and comparative analysis

Aspect-based sentiment analysis breaks down text according to individual aspects, features, or entities mentioned, rather than giving the whole text a sentiment score. For example, in the review “The lipstick didn’t match the color online,” an aspect-based sentiment analysis model would identify a negative sentiment about the color of the product specifically. To proficiently identify sentiment within the translated text, a comprehensive consideration of these language-specific features is imperative, necessitating the application of specialized techniques.

Figure 14 provides the confusion matrix for CNN-BI-LSTM, each entry in a confusion matrix denotes the number of predictions made by the model where it classified the classes correctly or incorrectly. Out of the 500-testing dataset available for testing, CNN-BI-LSTM correctly predicted 458 of the sentiment sentences. The Misclassification Rate is also known as Classification Error shows the fraction of predictions that were incorrect.

This transformer recently achieved a great performance in Natural language processing. Due to an absence of models that have already been trained in German, BERT is used to identify offensive language in German-language texts has so far failed. This BERT model is fine-tuned using 12 GB of German literature in this work for identifying offensive language.

Moreover, the unstructured nature of YouTube comments presents challenges for analysis, but recurrent neural networks (RNNs) excel in sequence learning, capturing subtle sentiments and enhancing their value for platforms such as YouTube and social media11. Today, semantic analysis methods are extensively used by language translators. Earlier, tools such as Google translate were suitable for word-to-word translations.

Continuous updates ensure the hybrid model improves over time, enhancing its ability to accurately reflect customer opinions. Sentiment analysis is a transformative tool in the realm of chatbot interactions, enabling more nuanced and responsive communication. By analyzing the emotional tone behind user inputs, chatbots can tailor their responses to better align with the user’s mood and intentions. Idioms represent phrases in which the figurative meaning deviates from the literal interpretation of the constituent words. Translating idiomatic expressions can be challenging because figurative connotations may not appear immediately in the translated text.

These models were capable of capturing distributed representations of words, but they were limited in their ability to handle large vocabularies. Traditional methods of representing words in a way that machines can understand, such as one-hot encoding, represent each word as a sparse vector with a dimension equal to the size of the vocabulary. Here, only one element of the vector is “hot” (set to 1) to indicate the presence of that word. While simple, this approach suffers from the curse of dimensionality, lacks semantic information and doesn’t capture relationships between words. BERT has been shown to outperform other NLP libraries on a number of sentiment analysis benchmarks, including the Stanford Sentiment Treebank (SST-5) and the MovieLens 10M dataset.

It also helps individuals identify problem areas and respond to negative comments10. Metadata, or comments, can accurately determine video popularity using computer linguistics, text mining, and sentiment analysis. YouTube comments provide valuable information, allowing for sentiment analysis in natural language processing11.

Table 5 shows that translated texts’ syntactic subsumption features of CT are higher than those of CO. This suggests that in CT, argument structures and sentences typically feature more and longer semantic roles than in CO. From these results we can infer that sentences in CT may have a more complex and condensed syntactic-semantic structure with a higher density of semantic roles in argument structures as well as sentences than in CO. Table 2 shows that the average number of semantic roles per sentence (ANPS) of CT is approximately the same as that of ES. However, CT’s average number of semantic roles per verb (ANPV) and average role length (ARL) are significantly lower than those of ES. This suggests that argument structures in CT normally contain semantic roles that are fewer and shorter than those in ES.

The discursive use of stability is profoundly rooted in China’s traditional political philosophies concerning the right to rule and the ethics of governance. According to these fundamental principles, the divine source of authority (Heaven) legitimized the right to govern China’s rulers and emperors, who were obligated to make decisions in the best interests of their people and even share their recreational parks. A leader who violated the principle of beneficence would be deposed and replaced by the populace.

Table 3 indicates that significant differences between CT and ES can be observed in almost all the features of the semantic roles. First, the values of ANPV and ANPS of agents (A0) in CT are significantly higher than those in ES, suggesting that Chinese argument structures and sentences usually contain more agents. This could serve as evidence for translation explicitation, in which the translator adds the originally omitted sentence subject to the translation and make the subject-verb relationship explicit. On the other hand, all the syntactic subsumption features (ANPV, ANPS, and ARL) for A1 and A2 in CT are significantly lower in value than those in ES.

In contrast, this multi-layered nested structure is deconstructed and decomposed in translated texts through the divide translation, and the number of sub-structures contained in each semantic role is controlled no greater than 1. This example proves that the informational structures in the translated texts are significantly simplified by reducing the number of nested sub-structures in semantic roles. Since the translation universal hypothesis was introduced (Baker, 1993), it has been a subject of constant debate and refinement among researchers in the field. On the one hand, some proposed that translation universals can be further divided into T-universals and S-universals (Chesterman, 2004). T-universals are concerned with the intralinguistic comparison between translated texts and non-translated original texts in the target language while S-universals are concerned with the interlinguistic comparison between source texts and translated texts.

Nonetheless, it is imperative for further studies to enhance these models and tools for semantic labelling and analysis, so as to promote a deeper understanding of semantic structures across different text types and languages. The concept of “the third language” was initially put forward by Duff (1981) to indicate that translational language can be distinguished from both the source language and the target language based semantic analysis of text on some of its intrinsic linguistic features. Frawley (2000) also introduced a similar concept known as “the third code” to emphasize the uniqueness of translational language generated from the process of rendering coded elements into other codes. The question of whether translational language should be regarded as a distinctive language variant has since sparked considerable debate in the field of translation studies.

Both proposed models, leveraging LibreTranslate and Google Translate respectively, exhibit better accuracy and precision, surpassing 84% and 80%, respectively. Compared to XLM-T’s accuracy of 80.25% and mBERT’s 78.25%, these ensemble approaches demonstrably improve sentiment identification capabilities. The Google Translate ensemble model garners the highest overall accuracy (86.71%) and precision (80.91%), highlighting its potential for robust sentiment analysis tasks. The consistently lower specificity across all models underscores the shared challenge of accurately distinguishing neutral text from positive or negative sentiment, requiring further exploration and refinement. 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.

You can also easily navigate through the different emotions behind a text or categorize them based on predefined and custom criteria. IBM Watson Natural Language Understanding (NLU) is an AI service for advanced text analytics that leverages deep learning to extract meaning and valuable insights from unstructured data. It can support up to 13 languages and extract metadata from texts, including entities, keywords, categories, sentiments, relationships, and syntax. Users can train a model using IBM Watson Knowledge Studio to understand the language of their business and generate customized and real-time insights. For specific sub-hypotheses, explicitation, simplification, and levelling out are found in the aspects of semantic subsumption and syntactic subsumption. However, it is worth noting that syntactic-semantic features of CT show an “eclectic” characteristic and yield contrary results as S-universals and T-universals.

This approach can also help reduce bias by removing human subjectivity from the process of analysis. The use of machine learning models and sentiment analysis techniques allows for more accurate identification and classification of different types of sexual harassment than traditional methods such as manual coding or human annotation. Lexicon-based sentiment and emotion allow for more nuanced analysis by taking into account the emotional context surrounding instances of sexual harassment. Finally, an LSTM-GRU deep learning model allows for a deeper understanding of the underlying factors that contribute to sexual harassment, which can inform future prevention and intervention efforts. The use of lexicon-based sentiment and emotion analysis, as well as a neural network, can help identify patterns and reduce bias in the analysis process.

The study was conducted under the Departmental IRB guidelines and was ruled “exempt”. GRU uses gating units that influence the flow of information within the unit to address the vanishing gradient problem of a regular RNN. GRU like LSTM has gating units that regulate data flow but unlike LSTM there is no need for additional designated memory cells.

Concurrence entanglement measure of the two-qubit cognitive state can be compared with quantification of semantic connection by Bell-like inequality introduced in114. Use of different Pauli operators in (8) may account for distinction between classical and quantum-like aspects of semantics102. Learning a concept apple, for example, amounts to configuring a specialized neuronal pattern that is reliably activated by appropriate complexes of visual, touch, taste, and smell signals79 and properly connected to other concepts80. This cognitive instrument allows an individual to distinguish apples from the background and use them at his or her discretion; this makes corresponding sensual information useful, i.e. meaningful for a subject81,82,83,84. Registry of such meaningful, or semantic, distinctions, usually expressed in natural language, constitutes a basis for cognition of living systems85,86.

The datasets generated or analyzed during this study are available from the corresponding author on reasonable request. Conceived the study, conducted the majority of the experiments, and wrote the main manuscript text. Provided critical feedback and helped shape the research, analysis, and manuscript.