Character gated recurrent neural networks for Arabic sentiment analysis Scientific Reports

By highlighting these contributions, this study demonstrates the novel aspects of this research and its potential impact on sentiment analysis and language translation. Australian startup Servicely develops Sofi, an AI-powered self-service automation software solution. Its self-learning AI engine uses plain English to observe and add to its knowledge, which improves its efficiency over time. This allows Sofi to provide employees and customers with more accurate information.

That’s why Blue Orange Digital worked with a hedge fund to optimize their human resources process. Using ten years’ worth of applicant data and resumes, the firm now has a sophisticated scoring model to find good-fit candidates. For instance, we may sarcastically use a word, which is often considered positive in the convention of communication, to express our negative opinion.

Branding can help a company improve its recognition, trust, and loyalty among customers as well as the effects of advertising, Forbes says. Now that no “generally best” method is found, we probe into how different models would benefit differently from various preprocessing methods. The following graph depicts the percentage improvement of using a certain preprocessing method compared with removing emojis at the beginning. For comparison among all encoder models, the results are shown in the bar chart above.

Manual data labeling takes a lot of unnecessary time and effort away from employees and requires a unique skill set. With that said, companies can now begin to explore solutions that sort and label all the relevant data points within their systems to create a training dataset. With the help of artificial intelligence, text and human language from all these channels can be combined to provide real-time insights into various aspects of your business. These insights can lead to more knowledgeable workers and the ability to address specific situations more effectively. Though having similar uses and objectives, stemming and lemmatization differ in small but key ways. Literature often describes stemming as more heuristic, essentially stripping common suffixes from words to produce a root word.

Performance evaluation

Similarly, the data from accounting, auditing, and finance domains are being analyzed using NLP to gain insight and inference for knowledge creation. Fisher et al.9 have presented work that used NLP in the accounting domain and provided future paths. Apart from these, Vinyals et al.10 have developed a new strategy for solving the problem of variable-size output dictionaries. Before determining employee sentiment, an organization must find a way to collect employee data.

Feedback provided by these tools is unbiased because sentiment analysis directly analyzes words frequently used to express positivity or negativity. Project managers can then continuously adjust how they communicate and steer the project by leveraging the numeric values assigned to different processes. Stemming is a text preprocessing technique in natural language processing (NLP).

• RoBERTa predicts 1602 correctly identified mixed feelings comments in sentiment analysis and 2155 correctly identified positive comments in offensive language identification.
• Rule-based systems are simple and easy to program but require fine-tuning and maintenance.
• Currently, NLP-based solutions struggle when dealing with situations outside of their boundaries.
• Still, applying the results of sentiment analysis in an appropriate scenario can be another scientific problem.

Unlike feedforward neural networks that employ the learned weights for output prediction, RNN uses the learned weights and a state vector for output generation16. Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long-Short Term Memory (Bi-LSTM), and Bi-directional Gated Recurrent Unit (Bi-GRU) are variants of the simple RNN. In recent years, classification of sentiment analysis in text is proposed by many researchers using different models, such as identifying sentiments in code-mixed data9 using an auto-regressive XLNet model. Despite the fact that the Tamil-English ChatGPT mixed dataset has more samples, the model is better on the Malayalam-English dataset; this is due to greater noise in the Tamil-English dataset, which results in poor performance. These results can be improved further by training the model for additional epochs with text preprocessing steps that includes oversampling and undersampling of the minority and majority classes, respectively10. Sentiment analysis uses machine learning techniques like natural language processing (NLP) and other calculations such as biometrics to determine if specific data is positive, negative or neutral.

Pre-processing of data

The authors showed that using machine-translated data can help distinguish relevant features for sentiment classification better using SVM models with Bag-of-N-Grams. The data-augmentation technique used in this study involves machine translation is sentiment analysis nlp to augment the dataset. Specifically, the authors used a pre-trained multilingual transformer model to translate non-English tweets into English. They then used these translated tweets as additional training data for the sentiment analysis model.

A comparative study was conducted applying multiple deep learning models based on word and character features37. Three CNN and five RNN networks were implemented and compared on thirteen reviews datasets. Although the thirteen datasets included reviews, the deep models performance varied according to the domain and the characteristics of the dataset. Based on word-level features Bi-LSTM, GRU, Bi-GRU, and the one layer CNN reached the highest performance on numerous review sets, respectively.

Similarly, the area under the ROC curve (AUC-ROC)60,171,172 is also used as a classification metric which can measure the true positive rate and false positive rate. In some studies, they can not only detect mental illness, but also score its severity122,139,155,173. 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.

For the task of mental illness detection from text, deep learning techniques have recently attracted more attention and shown better performance compared to machine learning ones116. The deep learning segment is projected to witness a higher growth rate during the forecast period. Deep Learning has played a critical role in advancing NLP developments in the finance sector. One of the main advantages of deep Learning is its ability to learn from large and complex datasets, which is particularly important in finance, where a vast amount of data is available.

Emma Strubell et al.8 , in their research work, when authors have used large amounts of unlabeled data. It has been observed that NLP in combination with a neural network model yielded good accuracy results, and the cost of computational resources determines the accuracy improvement. Based on extensive research, the author has also made some cost-cutting recommendations. Employee sentiment analysis enables HR to more easily and effectively obtain useful insights about what employees think about the organization by analyzing how they communicate in their work environment. 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.

In the media industry, it can be used for content generation and summarization. In the healthcare industry, it can be used for analyzing patient feedback and symptoms description. In the education sector, it can be used for personalized learning and tutoring. The code above specifies that we’re loading the EleutherAI/gpt-neo-2.7B model from Hugging Face Transformers for question answering. This pre-trained model can answer a wide variety of questions given some input.

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One advantage of Google Translate NMT is its ability to handle complex sentence structures and subtle nuances in language. Some methods combining several neural networks for mental illness detection have been used. For examples, the hybrid frameworks of CNN and LSTM models156,157,158,159,160 are able to obtain both local features and long-dependency features, which outperform the individual CNN or LSTM classifiers used individually.

This capability provides marketers with key insights to influence product strategies and elevate brand satisfaction through AI customer service. Its ability to understand the intricacies of human language, including context and cultural nuances, makes it an integral part of AI business intelligence tools. As standard in these recent pre-training times, we fine-tuned a BERT model with our proposed data set. BERT is one of the most popular neural architectures in Natural Language Processing.

Unstructured data comes in different formats and types, such as text, images, and videos, making extracting meaningful insights challenging. Financial institutions often rely on manual processing, which can be time-consuming, expensive, and prone to errors. You can foun additiona information about ai customer service and artificial intelligence and NLP. The NLP in finance market is estimated to witness significant growth during the forecast period, attributed to the increasing demand for automated and efficient financial services.

Specifically, the authors build graph neural networks by integrating SenticNet’s affective knowledge to improve sentence dependency graphs. To gather and analyze employee sentiment data at a sufficiently large scale, many organizations turn to employee sentiment analysis software that uses AI and machine learning to automate the process. NLP helps uncover critical insights from social conversations brands have with customers, as well as chatter around their brand, through conversational AI techniques and sentiment analysis. Goally used this capability to monitor social engagement across their social channels to gain a better understanding of their customers’ complex needs. Topic clustering through NLP aids AI tools in identifying semantically similar words and contextually understanding them so they can be clustered into topics.

The percentage in the following graph indicates the sentiment classification accuracy. Each cell represents the accuracy of an encoder model with a certain preprocessing method. Meta-feature (meta) Instead of treating emojis as part of the sentence, we can also regard them as high-level features. We use the Emoji Sentiment Ranking [4] lexicon to get the positivity, neutrality, negativity, and sentiment score features. Then, we concatenate those features with the emoji vector representations, which form the emoji meta-feature vector of the tweet. Pure text will be as usual passed through the encoder and BiLSTM layer, then the meta-feature vector will be concatenated with the last hidden states from the BiLSTM layer to be the input of the feedforward layers.

Top Data Classification Trends

There are various resources available online, including tutorials, documentation, and community forums, that can help you get started. You will also need a suitable dataset for training or fine-tuning the model, depending on your specific use case. 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.

Sample outputs from our sentiment analysis task are illustrated in Table 6. Offensive language is identified by using a pretrained transformer BERT model6. 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.

Interpreting VADER’s Polarity Scores

Sentiment analysis is analytical technique that uses statistics, natural language processing, and machine learning to determine the emotional meaning of communications. On October 7, Hamas launched a multipronged attack against Israel, targeting border villages and extending checkpoints around the Gaza Strip. The attack used armed rockets, expanded checkpoints, and helicopters to infiltrate towns and kidnap Israeli civilians, including children and the elderly1.

The first value at index [0] is the pseudo-probability of class negative, and the second value at [1] is the pseudo-probability of class positive. Dealing with misspellings is one of dozens of issues that make NLP problems difficult. The demo program loads the training data into a meta-list using a specific format that is required by the EmbeddingBag class. The meta-list of training data is passed to a PyTorch DataLoader object which serves up training data in batches. Behind the scenes, the DataLoader uses a program-defined collate_data() function, which is a key component of the system.

Sentiment analysis also enables service providers to analyze patient feedback to improve their satisfaction and overall experience. Sentiment analysis has become a valuable tool for organizations in a wide range of industries. Companies can use it for social media monitoring, customer service management, and analysis of customer data to improve operations and drive growth. Learn more about our picks in our review of the best sentiment analysis tools for 2024. IBM Watson NLU stands out as a sentiment analysis tool for its flexibility and customization, especially for users who are working with a massive amount of unstructured data.

Offensive language is any text that contains specific types of improper language, such as insults, threats, or foul phrases. This problem has prompted various researchers to work on spotting inappropriate communication on social media sites in order to filter data and encourage positivism. The earlier seeks to identify ‘exploitative’ sentences, which are regarded as a kind of degradation6. Stemming is one of several text normalization techniques that converts raw text data into a readable format for natural language processing tasks.

It is utilized by big companies like Spotify, and there are many benefits to using it. For one, it is highly useful for classical machine learning algorithms, such as those for spam detection, image recognition, prediction-making, and customer segmentation. Another experiment was conducted to evaluate the ability of the applied models to capture language ChatGPT App features from hybrid sources, domains, and dialects. The models trained on the mixed dataset are tested using the BRAD test set. The Bi-GRU-CNN model reported the highest performance on the BRAD test set, as shown in Table 8. Results prove that the knowledge learned from the hybrid dataset can be exploited to classify samples from unseen datasets.

From the figure, it can see that F1-Score, which is the harmonic mean of precision & recall, has a value of 74 %. Table 2 gives the details of experimental set up for performing simulation for the proposed work. The process of grouping related word forms that are from the exact words is known as Lemmatization, and with Lemmatization, we analyze those words as a single word. Stops Words (Words that connect other words and don’t provide a wider context) can be ignored and screened from the text as they are more standard and contain less useful knowledge. For example, conjunctions like ‘and’, ‘or’ and ‘but’, prepositions like ‘in’, ‘of’, ‘to’, ‘from’, and many others like the articles like ‘a’, ‘an’, and ‘the’. Commas and other punctuation may not be necessary for understanding the sentence’s meaning, so they are removed.