Character gated recurrent neural networks for Arabic sentiment analysis Scientific Reports

SpaCy is an open-source NLP library explicitly designed for production usage. SpaCy enables developers to create applications that can process and understand huge volumes of text. The Python library is often used to build natural language understanding systems and information extraction systems. Python is widely considered the best programming language, and it is critical for artificial intelligence (AI) and machine learning tasks.

Below are some of the key concepts and developments that have made using word embeddings such a powerful technique in helping advance NLP. Actual word embeddings typically have hundreds of dimensions to capture more intricate relationships and nuances in meaning. Word embeddings contribute to the success of question answering systems by enhancing the understanding of the context in which questions are posed and answers are found. Run the model on one piece of text first to understand what the model returns and how you want to shape it for your dataset. Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection. One of the tool’s features is tagging the sentiment in posts as ‘negative, ‘question’ or ‘order’ so brands can sort through conversations, and plan and prioritize their responses.

There is a dropout layer was added for LSTM and GRU, respectively, to reduce the complexity. The model had been trained using 20 epochs and the history of the accuracy and loss had been plotted and shown in Fig. To avoid overfitting, the 3 epochs were chosen as the final model, where the prediction accuracy is 84.5%. Next, monitor performance and check if you’re getting the analytics you need to enhance your process. You can foun additiona information about ai customer service and artificial intelligence and NLP. Once a training set goes live with actual documents and content files, businesses may realize they need to retrain their model or add additional data points for the model to learn.

Yet Another Twitter Sentiment Analysis Part 1 — tackling class imbalance

We’ve gone over several options for transforming text that can improve the accuracy of an NLP model. Which combination of these techniques will yield the best results will depend on the task, data representation, and algorithms you choose. It’s always a good idea to try out many different combinations to see what works. Recall that linear classifiers tend to work well on very sparse datasets (like the one we have). Another algorithm that can produce great results with a quick training time are Support Vector Machines with a linear kernel. Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction techniques that follows the same method as Singular Value Decomposition.

Note that VADER breaks down sentiment intensity scores into a positive, negative and neutral component, which are then normalized and squashed to be within the range [-1, 1] as a “compound” score. As we add more exclamation marks, capitalization and emojis/emoticons, the intensity gets more and more extreme (towards +/- 1). I selected a few sentences with the most noticeable particularities between the Gold-Standard (human scores) and ChatGPT. Then, I used the same threshold established previously to convert the numerical scores into sentiment labels (0.016).

Computational literary studies, a subfield of digital literary studies, utilizes computer science approaches and extensive databases to analyse and interpret literary texts. Through the application of quantitative methods and computational power, these studies aim to uncover insights regarding the structure, trends, and patterns within the literature. The field of digital humanities offers diverse and substantial perspectives on social situations. While it is important to note that predictions made in this field may not be applicable to the entire world, they hold significance for specific research objects. For example, in computational linguistics research, the lexicons used in emotion analysis are closely linked to relevant concepts and provide accurate results for interpreting context. However, it is important to acknowledge that embedded dictionaries and biases may introduce exceptions that cannot be completely avoided.

Setup

A simple explanation is that one can potentially express more positive or negative emotions with more words. Of course, the scores cannot be more than 1, and they saturate eventually (around 0.35 here). Please note that I reversed the sign of NSS values to better depict this for both PSS and NSS. Another hybridization paradigm is combining word embedding and weighting techniques. Combinations of word embedding and weighting approaches were investigated for sentiment analysis of product reviews52.

There are a number of different NLP libraries and tools that can be used for sentiment analysis, including BERT, spaCy, TextBlob, and NLTK. 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. In other words, semantic analysis is the technical practice that enables the strategic practice of sentiment analysis. You then use sentiment analysis tools to determine how customers feel about your products or services, customer service, and advertisements, for example.

Machine learning algorithm-based automated semantic analysis

They range from virtual agents and sentiment analysis to semantic search and reinforcement learning. 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. Lexicon based approaches use sentiment lexicons that contain words and their corresponding sentiment scores.

The rapid growth of social media and digital data creates significant challenges in analyzing vast user data to generate insights. Further, interactive automation systems such as chatbots are unable to fully replace humans due to their lack of understanding of semantics ChatGPT and context. To tackle these issues, natural language models are utilizing advanced machine learning (ML) to better understand unstructured voice and text data. This article provides an overview of the top global natural language processing trends in 2023.

• Therefore, after the models are trained, their performance is validated using the testing dataset.
• Platforms such as Twitter, Facebook, YouTube, and Snapchat allow people to express their ideas, opinions, comments, and thoughts.
• Please note that I reversed the sign of NSS values to better depict this for both PSS and NSS.
• 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 of the model in real-world applications93.
• Rocchio classification uses the frequency of the words from a vector and compares the similarity of that vector and a predefined prototype vector.
• BERT is the most accurate of the four libraries discussed in this post, but it is also the most computationally expensive.

For example, CNNs were applied for SA in deep and shallow models based on word and character features19. Moreover, hybrid architectures—that combine RNNs and CNNs—demonstrated the ability to consider the sequence components order and find out the context features in sentiment analysis20. These architectures stack layers of CNNs and gated RNNs in various arrangements such as CNN-LSTM, CNN-GRU, LSTM-CNN, GRU-CNN, CNN-Bi-LSTM, CNN-Bi-GRU, Bi-LSTM-CNN, and Bi-GRU-CNN. Convolutional layers help capture more abstracted semantic features from the input text and reduce dimensionality.

Comprehend’s advanced models can handle vast amounts of unstructured data, making it ideal for large-scale business applications. It also supports custom entity recognition, enabling users to train it to detect specific terms relevant to their industry or business. Another plausible constraint pertains to the practicality and feasibility of translating foreign language text, particularly in scenarios involving extensive text volumes or languages that present significant challenges.

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). Moreover, researchers have sought to supplement their findings by examining evidence from alternative sources such as literary texts and life writings. Consequently, the task of extracting specific content from extensive texts like novels is arduous and time-consuming. The scholarly community has made substantial progress in comprehending the multifaceted nature of sexual harassment cases in the Middle East (Karami et al., 2021). Researchers have conducted rigorous empirical studies that shed light on various aspects of this issue, including its prevalence rates, underlying causes, and societal implications (Bouhlila, 2019).

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). One of the main challenges in traditional manual text analysis is the inconsistency in interpretations resulting from the abundance of information and individual emotional and cognitive biases. Human misinterpretation and subjective interpretation often lead to errors in data analysis (Keikhosrokiani and Asl, 2022; Keikhosrokiani and Pourya Asl, 2023; Ying et al., 2022).

There are six machine learning algorithms are leveraged to build the text classification models. K-nearest neighbour (KNN), logistic regression (LR), random forest (RF), multinomial naïve Bayes (MNB), stochastic gradient descent (SGD) and support vector classification (SVC) are built. The first layer of LSTM-GRU is an embedding layer with m number of vocab and n output dimension.

Also, Convolution Neural Networks (CNNs) were efficiently applied for implicitly detecting features in NLP tasks. In the proposed work, different deep learning architectures composed of LSTM, GRU, Bi-LSTM, and Bi-GRU are used and compared for Arabic sentiment analysis performance improvement. The models are implemented and tested based on the character representation of opinion entries. Moreover, deep hybrid models that combine multiple layers of CNN with LSTM, GRU, Bi-LSTM, and Bi-GRU are also tested. Two datasets are used for the models implementation; the first is a hybrid combined dataset, and the second is the Book Review Arabic Dataset (BRAD). The proposed application proves that character representation can capture morphological and semantic features, and hence it can be employed for text representation in different Arabic language understanding and processing tasks.

The key difference between the FastText and SVM results is the percentage of correct predictions for the neutral class, 3. The SVM predicts more items correctly in the majority classes (2 and 4) than FastText, which highlight the weakness of feature-based approaches in text classification problems with imbalanced semantic analysis nlp classes. Word embeddings and subword representations, as used by FastText, inherently give it additional context. This is especially true when it comes to classifying unknown words, which are quite common in the neutral class (especially the very short samples with one or two words, mostly unseen).

In our review, we report the latest research trends, cover different data sources and illness types, and summarize existing machine learning methods and deep learning methods used on this task. 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. LSA simply tokenizer the words in a document with TF-IDF, and then compressed these features into embeddings with SVD.

Text Representation Models in NLP

The bag-of-words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification. Question answering involves answering questions posed in natural language by generating appropriate responses. This task has various applications such as customer support chatbots and educational platforms. The above command tells FastText to train the model on the training set and validate on the dev set while optimizing the hyper-parameters to achieve the maximum F1-score. It is thus important to remember that text classification labels are always subject to human perceptions and biases.

• Developers can also access excellent support channels for integration with other languages and tools.
• There are many aspects that make Python a great programming language for NLP projects, including its simple syntax and transparent semantics.
• Talkwalker has a simple and clean dashboard that helps users monitor social media conversations about a new product, marketing campaign, brand reputation, and more.
• The process of classifying and labeling POS tags for words called parts of speech tagging or POS tagging .
• Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks.

It systematically analyzes textual content to determine whether it conveys positive, negative, or neutral sentiments. The general area of sentiment analysis has experienced exponential growth, driven primarily by the expansion of digital communication platforms and massive amounts of daily text data. However, the effectiveness of sentiment analysis has primarily been demonstrated in English owing to the availability of extensive labelled datasets and the development of sophisticated language models6. This leaves a significant gap in analysing sentiments in non-English languages, where labelled data are often insufficient or absent7,8. However, the current train set consists of only 70 sentences, which is relatively small. This limited size can make the model sensitive and prone to overfitting, especially considering the presence of highly frequent words like ‘rape’ and ‘fear’ in both classes.

Comparing SDG and KNN, SDG outperforms KNN due to its higher accuracy and strong predictive capabilities for both physical and non-physical sexual harassment. Table 9 presents the sentences that have been labelled as containing sexually harassing words, along with the corresponding keywords ChatGPT App detected through a rule-based approach. For instance, in the first sentence, the word ‘raped’ is identified as a sexual word. This sentence describes a physical sexual offense involving coercion between the victim and the harasser, who demands sexual favours from the victim.

Moreover, the Gaza conflict has led to widespread destruction and international debate, prompting sentiment analysis to extract information from users’ thoughts on social media, blogs, and online communities2. Israel and Hamas are engaged in a long-running conflict in the Levant, primarily centered on the Israeli occupation of the West Bank and Gaza Strip, Jerusalem’s status, Israeli settlements, security, and Palestinian freedom3. Moreover, the conflict in Hamas emerged from the Zionist movement and the influx of Jewish settlers and immigrants, primarily driven by Arab residents’ fear of displacement and land loss4. Additionally, in 1917, Britain supported the Zionist movement, leading to tensions with Arabs after WWI. The Arab uprising in 1936 ended British support, resulting in Arab independence5.

Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. The reason vectors are used to represent words is that most machine learning algorithms, including neural networks, are incapable of processing plain text in its raw form. Built primarily for Python, the library simplifies working with state-of-the-art models like BERT, GPT-2, RoBERTa, and T5, among others.

Relationship Extraction & Textual Similarity

These cells function as gated units, selectively storing or discarding information based on assigned weights, which the algorithm learns over time. This adaptive mechanism allows LSTMs to discern the importance of data, enhancing their ability to retain crucial information for extended periods28. A ‘search autocomplete‘ functionality is one such type that predicts what a user intends to search based on previously searched queries. It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result. All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost.

This lets HR keep a close eye on employee language, tone and interests in email communications and other channels, helping to determine if workers are happy or dissatisfied with their role in the company. After these scores are aggregated, they’re visually presented to employee managers, HR managers and business leaders using data visualization dashboards, charts or graphs. Being able to visualize employee sentiment helps business leaders improve employee engagement and the corporate culture.

The applied word2vec word embedding was trained on a large and diverse dataset to cover several dialectal Arabic styles. For the sentiment classification, a deep learning model LSTM-GRU, an LSTM ensemble with GRU Recurrent neural network (RNN) had been leveraged to classify the sentiment analysis. There are about 60,000 sentences in which the labels of positive, neutral, and negative are used to train the model. RNNs are a type of artificial neural network that excels in handling sequential or temporal data. In the case of text data, RNNs convert the text into a sequence, enabling them to capture the relationship between words and the structure of the text.

The fore cells handle the input from start to end, and the back cells process the input from end to start. The two layers work in reverse directions, enabling to keep the context of both the previous and the following words47,48. As delineated in the introduction section, a significant body of scholarly work has focused on analyzing the English translations of The Analects. However, the majority of these studies often omit the pragmatic considerations needed to deepen readers’ understanding of The Analects. Given the current findings, achieving a comprehensive understanding of The Analects’ translations requires considering both readers’ and translators’ perspectives.

This solution consolidates data from numerous construction documents, such as 3D plans and bills of materials (BOM), and simplifies information delivery to stakeholders. There is a growing interest in virtual assistants in devices and applications as they improve accessibility and provide information on demand. However, they deliver accurate information only if the virtual assistants understand the query without misinterpretation.

Best NLP Tools ( : AI Tools for Content Excellence

This method systematically searched for optimal hyperparameters within subsets of the hyperparameter space to achieve the best model performance. The specific subset of hyperparameters for each algorithm is presented in Table 11. Deep learning enhances the complexity of models by transferring data using multiple functions, allowing hierarchical representation through multiple levels of abstraction22. Additionally, this approach is inspired by the human brain and requires extensive training data and features, eliminating manual selection and allowing for efficient extraction of insights from large datasets23,24. In order to train a good ML model, it is important to select the main contributing features, which also help us to find the key predictors of illness.

Tags enable brands to manage tons of social posts and comments by filtering content. They are used to group and categorize social posts and audience messages based on workflows, business objectives and marketing strategies. As a result, they were able to stay nimble and pivot their content strategy based on real-time trends derived from Sprout. This increased their content performance significantly, which resulted in higher organic reach. NLP algorithms detect and process data in scanned documents that have been converted to text by optical character recognition (OCR). This capability is prominently used in financial services for transaction approvals.

This not only overcomes the simplifications seen in prior models but also broadens ABSA’s applicability to diverse real-world datasets, setting new standards for accuracy and adaptability in the field. In our approach to ABSA, we introduce an advanced model that incorporates a biaffine attention mechanism to determine the relationship probabilities among words within sentences. This mechanism generates a multi-dimensional vector where each dimension corresponds to a specific type of relationship, effectively forming a relation adjacency tensor for the sentence. To accurately capture the intricate connections within the text, our model converts sentences into a multi-channel graph. This graph treats words as nodes and the elements of the relation adjacency tensor as edges, thereby mapping the complex network of word relationships. These include lexical and syntactic information such as part-of-speech tags, types of syntactic dependencies, tree-based distances, and relative positions between pairs of words.

I am a researcher, and its ability to do sentiment analysis (SA) interests me. The search query we used was based on four sets of keywords shown in Table 1. For mental illness, 15 terms were identified, related to general terms for mental health and disorders (e.g., mental disorder and mental health), and common specific mental illnesses (e.g., depression, suicide, anxiety). For data source, we searched for general terms about text types (e.g., social media, text, and notes) as well as for names of popular social media platforms, including Twitter and Reddit.

Instead of prescriptive, marketer-assigned rules about which words are positive or negative, machine learning applies NLP technology to infer whether a comment is positive or negative. 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. However, for the experiment, this model was used in the baseline configuration and no fine tuning was done. Similarly, the dataset was also trained and tested using a multilingual BERT model called mBERT38.