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

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. While AI, ML, deep learning and neural networks are related technologies, the terms are often used interchangeably. Attention mechanisms were introduced to improve the ability of neural networks to focus on specific parts of the input sequence when making predictions. Instead of treating all parts of the input equally, attention mechanisms allow the model to selectively attend to relevant portions of the input. GloVe is based on the idea that the global statistics of word co-occurrence across the entire corpus are crucial for capturing word semantics.

Their results indicate that the topic-specific sentiment, frequency and “surprise” of news text can predict future returns, volatility, and drawdowns. Hybrid approaches combine rule-based and machine-learning techniques and usually result in more accurate sentiment analysis. For example, a brand could train an algorithm on a set of rules and customer reviews, updating the algorithm until it catches nuances specific to the brand or industry.

1. Sentiment from the headlines

In contrast to NearMiss-1, NearMiss-2 keeps those points from the majority class whose mean distance to the k farthest points in minority class is lowest. In other words, it will keep the points of majority class that’s most different to the minority class. It seems like both the accuracy and F1 score got worse than random undersampling. If we oversample the minority class in the above oversampling, with downsampling, we try to reduce the data of majority class, so that the data classes are balanced.

Stanford CoreNLP is written in Java and can analyze text in various programming languages, meaning it’s available to a wide array of developers. Indeed, it’s a popular choice for developers working on projects that involve complex processing and understanding natural language text. Another feature that made VADER the right tool for our experiments is that its sentiment analyzer can handle negations and UTF-8-encoded emojis, as well as acronyms, slang and punctuation. Furthermore, it takes punctuation into account by amplifying the sentiment score of the sentence proportionally to the number of exclamation points and question marks ending the sentence. If the score is positive then VADER adds a certain empirically-obtained score for every exclamation point (0.292) and question mark (0.18).

This guide will introduce you to some basic concepts you need to know to get started with this straightforward programming language. IBM watsonx is a portfolio of business-ready tools, applications and solutions, designed to reduce the costs and hurdles of AI adoption while optimizing outcomes and responsible use of AI. Transformers allow for more parallelization during training compared to RNNs and are computationally efficient. Transformers use a self-attention mechanism to capture relationships between different words in a sequence. This mechanism allows each word to attend to all other words in the sequence, capturing long-range dependencies.

This not only optimizes the efficiency of solving cold start recommender problems but also improves recommendation quality. Search engines are an integral part of workflows to find and receive digital information. One of the barriers to effective searches is the lack of understanding of the context and intent of the input data. Hence, semantic search models find applications in areas such as eCommerce, academic research, enterprise knowledge management, and more. Latvian startup SummarizeBot develops a blockchain-based platform to extract, structure, and analyze text. It leverages AI to summarize information in real time, which users share via Slack or Facebook Messenger.

Conversely, the need to analyze short texts became significantly relevant as the popularity of microblogs, such as Twitter, grew. The challenge with inferring topics from short text is that it often suffers from noisy data, so it can be difficult to detect topics in a smaller corpus (Phan et al., 2011). Despite the vast amount of data available on YouTube, identifying and evaluating war-related comments can be difficult. Platform limits, as well as data bias, have the potential to compromise the dataset’s trustworthiness and representativeness.

Improved customer experience

Create a DataLoader class for processing and loading of the data during training and inference phase. Using subjective relevance judgment as observable for semantic connectivity can be seen as inverse of the basic objective of information retrieval science aiming to rank text documents according to the user’s needs. In this way, quantum approach allows to consider simple units of cognition while circumventing detailed description of the human’s mind and brain.

Besides, sentiment analysis and semantic search enable language processors to better understand text and speech context. Named entity recognition (NER) works to identify names and persons within unstructured data while text summarization reduces text volume to provide important key points. Language transformers are also advancing language processors through self-attention. Lastly, multilingual language models use machine learning to analyze text in multiple languages.

Natural Language Processing, word2vec, Support Vector Machine, bag-of-words, deep learning

Given the sheer volume of conversations happening on social media, investing in a social media tool with sentiment analysis capability becomes necessary. These tools simplify the otherwise time-consuming tasks related to sentiment analytics and help with targeted insights. The beauty of social media for sentiment analysis is that there’s so much data to gather. With more consumers tagging and talking about brands on social platforms, you can tap into real data showing how your brand performs over time and across core platforms where you have a social media presence. This actionable data can be used to identify trends, measure the effectiveness of your campaigns and understand customer preferences. The dataset was collected from various English News YouTube channels, such as CNN, Aljazeera, WION, BBC, and Reuters.

Syndicating content to external sites such as Medium and Linkedin can engage followers, but copying and pasting entire articles create duplicate content. ChatGPT makes it easy to repurpose content for followers and encourages them to read the full version on your site. All the popular NLP algorithms can be implemented via the library’s user-friendly interfaces, including algorithms such as Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA/LSI/SVD), and Random Projections (RP). This library is highly recommended for anyone relatively new to developing text analysis applications, as text can be processed with just a few lines of code.

Our extensive experiments on benchmark datasets show that the proposed approach achieves the state-of-the-art performance on all benchmark datasets. Our work clearly demonstrates that by leveraging DNN for feature extraction, GML can easily outperform the pure DNN solutions. Based on Maslow’s hierarchy of needs theory, this paper argues that danmaku text emotion is jointly generated by individual needs and external stimuli. Most machine learning algorithms applied for SA are mainly supervised approaches such as Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Networks (ANN), and K-Nearest Neighbor (KNN)26. But, large pre-annotated datasets are usually unavailable and extensive work, cost, and time are consumed to annotate the collected data.

Several methods can operate in the areas of information retrieval and text mining to perform keyword and topic extraction, such as MAUI, Gensim, and KEA. In the following, we give a brief description of the included TM methods in this comparison review. In this paper, we focused on five frequently used TM methods that are built using a diverse representation form and statistical models. 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.

An integrated Neo-Piagetian/Neo-Eriksonian development model II: RAF, qubit, and supra-theory modeling

Besides, the learning capability of deep architectures is exploited to capture context features from character encoded text. Pure Urdu lexicon list containing 4728 negative and 2607 positive opinion words are publicly available. Initially, each sentence is tokenized, and then each token is classified into one of three classes by comparing it to the available opinion words in the Urdu lexicon. The accessible Urdu lexicon and the words are used to determine the overall sentiment of the user review. If the text contains more positive tokens, the review is categorized as positive with a polarity score of 1. A review is characterized as negative with a polarity score of 2 if it contains more negative tokens (words) than positive tokens (words).

Our evaluation was based on four metrics, precision, recall, F1 score, and specificity. Our results indicate that Google Translate, with the proposed ensemble model, achieved the highest F1 score in all four languages. Our findings suggest that Google Translate is better at translating foreign languages into English. 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.

Overall, our correlation analysis shows that sentiment captured from headlines could be used as a signal to predict market returns, but not so much volatility. A correlation coefficient of –0.7, and p-value below 0.05 indicated that there is a strong negative correlation between positive sentiment captured from the tweets and the volatility of the market next day. It suggests that as the positive sentiment increases, market volatility decreases.

In the training phase, we randomly extract r labeled sentences from training data for each labeled sentence to fine-tune the semantic network. Then, in the feature extraction phase, we randomly extract r sentences from labeled training data for each unlabeled sentence in the target workload, and construct its relations w.r.t them based on the semantic network. Our experiments has demonstrated that the performance of supervised GML is very robust w.r.t the value of r provided that it is set within a reasonable range (\(3\le r\le 8\)). In addition, the Bi-GRU-CNN trained on the hyprid dataset identified 76% of the BRAD test set. Therefore, hybrid models that combine different deep architectures can be implemented and assessed in different NLP tasks for future work.

How We Evaluated Sentiment Analysis Tools

Sentiment analysis is an application of natural language processing (NLP) that reveals the emotional states in human speech or text — in this case, the speech and text that customers generate. Businesses can use machine-learning-based sentiment analysis software to examine this speech and text for positive or negative sentiment about the brand. With this information, companies have an opportunity to respond meaningfully — and with greater empathy. A recurrent neural network used largely for natural language processing is the bidirectional LSTM. It may use data from both sides and, unlike regular LSTM, input passes in both directions. Furthermore, it is an effective tool for simulating the bidirectional interdependence between words and expressions in the sequence, both in the forward and backward directions.

The CNN has pooling layers and is sophisticated because it provides a standard architecture for transforming variable-length words and sentences of fixed length distributed vectors. For sentence categorization, we utilize a minimal CNN convolutional network, however one channel is used to keep things simple. To begin, the sentence is converted into a matrix, with word vector representations in the rows of each word matrix. To obtain a length n vector from a convolution layer, a 1-max pooling function is employed per feature map. Finally, dropouts are used as a regularization method at the softmax layer28,29. In positive class labels, an individual’s emotion is expressed in the sentence as happy, admiring, peaceful, and forgiving.

Relative to the dichotomic alternative 0/1, potential outcomes of the experiment are encoded by superposition vector state \(\left| \Psi \right\rangle\) (1). If the experiment is performed, the system transfers to one of the superposed potential outcomes according to probabilities \(p_i\). In physical terms, control of the living system’s behavior is understood as electrochemical process occurring in an individual’s nervous system including \(\sim \)100 billion neuron cells interacting with each other via action potentials47.

Sexual harassment types

The data source for this study consists of twelve Middle Eastern novels written in English. Additionally, lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Lexicon-based sentiment analysis involves analysing text for positive or negative sentiment using pre-defined lexicons or dictionaries. Emotion analysis involves identifying emotions expressed within text, such as anger or sadness. Finally, an LSTM-GRU deep learning model is built to classify the sentiment characteristics that induce sexual harassment.

Hence CNN-Bidirectional-LSTM models are more suitable for sentiment classification. The aim of this article is to demonstrate how different information extraction techniques can be used for SA. But semantic analysis of text for the sake of simplicity, I’ll only demonstrate word vectorization (i.e tf-idf) here. As with any supervised learning task, the data is first divided into features (Feed) and label (Sentiment).

Instead than employing semantic information, these classifiers define class boundaries based on the discriminative power of words in relation to their classes. Similarly, SVM’s capacity to capture feature interactions to some extent makes it superior to NB, which typically treats features independently. The significance of sentiment analysis may be seen in our desire to know what they think and how others feel about the problem16.

Recently, transformer architectures147 were able to solve long-range dependencies using attention and recurrence. Wang et al. proposed the C-Attention network148 by using a transformer encoder block with multi-head self-attention and convolution processing. Zhang et al. also presented their TransformerRNN with multi-head self-attention149.

Performance statistics of mainstream baseline model with the introduction of the MIBE-based lexicon and the FF layer. Integrating these insights into your social strategy helps your brand remain responsive, customer-focused‌ and aligned with market expectations. This enriches your current operations and sets a solid foundation for long-term success.

• To begin, the sentence is converted into a matrix, with word vector representations in the rows of each word matrix.
• In the case of two distinctions, the perception model generates a two-qubit state, entanglement of which quantifies semantic connection between the corresponding words.
• For instance, we are using headlines from day t to predict the direction of movement (increase/decrease) of volatility the next day.
• In this section, we discuss the signs of cross-correlation and the results of the Granger causality tests used to identify the indicators that could anticipate the consumer confidence components (see Table 2).
• Then added a dropout layer to the Convolutional layer before feeding it into the pooling layer, then added a dense layer.

Different researchers used sentimental analysis for Amharic sentiment either with Lexical or Machine Learning. Both approaches require the interference of the programmer at one point or another. But when it comes to deep learning it minimizes human involvement which makes life easier. In this research, the researcher applied sentimental analysis on Amharic political sentences using four different deep learning approaches; CNN, Bi-LSTM, GRU, and hybrid of CNN with Bi-LSTM. To the researcher’s knowledge, this is the first work that applied BI-LSTM, GRU, and CNN-Bi-LSTM.

In the following subsections, we provide an overview of the datasets and the methods used. In section Datesets, we introduce the different types of datasets, which include different mental illness applications, languages and sources. Section NLP methods used to extract data provides an overview of the approaches and summarizes the features for NLP development. The trend of the number of articles containing machine learning-based and deep learning-based methods for detecting mental illness from 2012 to 2021. In addition, this study represents a substantial contribution to the limited literature on the application of sentiment analysis to CDA and news discourse analysis.

Four experiments were conducted by dividing the preprocessed dataset into three subsets which was 4000 sentences for training, 500 for validation, and another 500 for testing. The general Architecture of Amharic sentimental analysis using a deep learning approach is shown in Fig. Long short-term memory networks that are bidirectional can incorporate context information from both past and future inputs25. Over long sequences, parts of the gradient vector may exponentially expand or decline, making it challenging for RNN to include long-term dependencies. The LSTM design overcomes the issue of learning long-term dependencies presented by the simple RNN by incorporating a memory cell that can hold a state over a long period. In a way, the Bidirectional-LSTM combines the forward hidden layer with the backward hidden layer (see the Fig. 2), to manipulate both previous and future input.

A huge amount of data has been generated on social media platforms, which contains crucial information for various applications. As a result, sentiment analysis is critical for analyzing public perceptions of any product or service. In contrast, we proposed a multi-class Urdu sentiment analysis dataset and used various machine and deep learning ChatGPT algorithms to create baseline results. Additionally, our proposed mBERT classifier, achieves F1 score of 81.49% and 77.18% using UCSA and UCSA-21 datasets respectively. The primary purpose for using a set of machine learning algorithms with word and character n-gram features to establish baseline results against our proposed Urdu corpus.

With AI, users can comprehend how customers perceive a certain product or service by converting human language into a form that machines can interpret. We chose MonkeyLearn as one of the top sentiment analysis tools because it helps businesses access real-time analysis with easy integrations from third-party apps. This platform also enables users to trigger actions and set up rules based on sentiments, such as escalating negative cases, prioritizing positive comments, or tagging tickets. MonkeyLearn’s workflow integrations provide a holistic view of customer sentiments gathered from various sources, resulting in rich insights and more actionable data. The MLEGCN represents a significant development over traditional Graph Convolutional Networks (GCN), designed to process graph-structured data more effectively in natural language processing tasks.

The proposed model Adapter-BERT correctly classifies the 1st sentence into the positive sentiment class. It can be observed that the proposed model wrongly classifies it into the positive category. The reason for this misclassification may be because of the word “furious”, which the proposed model predicted as having a positive sentiment. If the model is trained based on not only words but also context, this misclassification can be avoided, and accuracy can be further improved. Similarly, the model classifies the 3rd sentence into the positive sentiment class where the actual class is negative based on the context present in the sentence.

Based on the results of textual entailment analysis, the study further investigates translation universals at the semantic level and collects evidence for the influence of the translation process on informational explicitness as well as the semantic structure. In order to visually compare the performance of each comparative model, this paper, based on Table 3, draws Fig. 7 (performance statistics of mainstream baseline model for sentiment analysis), Fig. 8 (performance statistics of mainstream baseline model with the introduction of the jieba lexicon and the FF layer), Fig. 9 (performance statistics of mainstream baseline model with the introduction of the MIBE-based lexicon and the FF layer), and Fig.

Meanwhile, many customers create and share content about their experience on review sites, social channels, blogs etc. The valuable information in the authors tweets, reviews, comments, posts, and form submissions stimulated the necessity of manipulating this massive data. The revealed information is an essential requirement to make informed business decisions. You can foun additiona information about ai customer service and artificial intelligence and NLP. Understanding individuals sentiment is the basis of understanding, predicting, and directing their behaviours. By applying NLP techniques, SA detects the polarity of the opinioned text and classifies it according to a set of predefined classes.

According to calculation of amplitudes described in “Results” section, cognitive model of the text (4) depends on its sentence structure. In particular, random shuffle of words and periods leads to factorization of state (4) and zero concurrence which reflects elimination of semantic connection. At the same time, calculation of amplitudes is not affected by shuffle of both sentences within text ChatGPT App and words within sentences, so that subsequent calculation of concurrence as measure of semantic connection is also invariant to these operations. The algorithm thereby treats text as a bag of sentences which may be paralleled with a bag of words level of text analysis146,147. This specifies level of semantics that can be detected as entanglement between corresponding cognitive representations.

The BiLSTM model performed second, and only learned simple temporal information without the support of pre-trained models. It was difficult to learn the deep and rich linguistic knowledge of danmaku texts. The BernoulliNB model performed the worst, as it required binarization of the data, which resulted in some information loss and affected the quality and integrity of the data. The use of machine learning approaches can help to identify patterns within large datasets that may not be immediately apparent through manual analysis.