Arabic Sentiment Analysis An Illustrative guide on how to perform by Dhikrullah Folorunsho

Both types of sexual harassment are often justified or normalized by the harassers as a way of expressing their masculinity and asserting their dominance. 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. These visualizations underscore the framework’s capacity to capture and quantify the syntactic essence of language. Attention mechanisms have revolutionized ABSA, enabling models to home in on text segments critical for discerning sentiment toward specific aspects64.

• Additionally, novel end-to-end methods for pairing aspect and opinion terms have moved beyond sequence tagging to refine ABSA further.
• Sentiment analysis, also known as opinion mining, is widely used to detect how customers feel about products, brands and services.
• The negative recall or specificity evaluates the network identification of the actual negative entries registered 0.89 with the GRU-CNN architecture.
• This forms the major component of all results in the semantic similarity calculations.
• The work by Salameh et al.10 presents a study on sentiment analysis of Arabic social media posts using state-of-the-art Arabic and English sentiment analysis systems and an Arabic-to-English translation system.
• Sociality can vary across different dimensions, such as social interaction, social patterns, and social activities within different data ages.

These graphical representations serve as a valuable resource for understanding how different combinations of translators and sentiment analyzer models influence sentiment analysis performance. Following the presentation of the overall experimental results, the language-specific experimental findings are delineated and discussed in detail below. NLP drives automatic machine translations of text or speech data from one language to another. NLP uses many ML tasks such as word embeddings and tokenization to capture the semantic relationships between words and help translation algorithms understand the meaning of words. An example close to home is Sprout’s multilingual sentiment analysis capability that enables customers to get brand insights from social listening in multiple languages.

NLP programs lay the foundation for the AI-powered chatbots common today and work in tandem with many other AI technologies to power the modern enterprise. Sentiment analysis helps you gain insights into customer feedback, brand perception, or public opinion to improve on your business’s weaknesses and expand on its strengths. The feedback can inform your approach, and the motivation and positive reinforcement from a great customer interaction can be just what a support agent needs to boost morale. EWeek has the latest technology news and analysis, buying guides, and product reviews for IT professionals and technology buyers.

Learning SVMs from examples

However, the choice of the model for specific applications should be aligned with the unique requirements of the task, considering the inherent trade-offs in precision, recall, and the complexities of natural language understanding. This study opens avenues for further research to enhance the accuracy and effectiveness of sentiment analysis models. The diverse opinions and emotions expressed in these comments are challenging to comprehend, as public opinion on war events can fluctuate rapidly due to public debates, official actions, or breaking news13. Managing hate speech and offensive remarks in war discussions on YouTube is crucial, requiring an understanding of user-generated content, privacy, and moral considerations, especially during wartime14,15.

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. Sentiment analysis, a crucial natural language processing task, involves the automated detection of emotions expressed in text, distinguishing between positive, negative, or neutral sentiments. Nonetheless, conducting sentiment analysis in foreign languages, particularly without annotated data, presents complex challenges9. While traditional approaches have relied on multilingual pre-trained models for transfer learning, limited research has explored the possibility of leveraging translation to conduct sentiment analysis in foreign languages.

Applied models

It is then trivial to compute the model’s accuracy and F1-scores by using the accuracy method defined in the Base class. The above example makes it clear why this is such a challenging dataset on which to make sentiment predictions. For example, annotators tended to categorize the phrase “nerdy folks” as somewhat negative, since the word “nerdy” has a somewhat negative connotation in terms of our society’s current perception of nerds. However, from a purely linguistic perspective, this sample could just as well be classified as neutral. One important aspect to note before analyzing a sentiment classification dataset is the class distribution in the training data.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Chatbots help customers immensely as they facilitate shipping, answer queries, and also offer personalized guidance and input on how to proceed further. Moreover, some chatbots are equipped with emotional intelligence that recognizes the tone of the language and hidden sentiments, framing emotionally-relevant responses to them. Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks.

The lexicon approaches are popularly used for Modern Standard Arabic (MSA) due to the lack of vernacular Arabic dictionaries6. Sentiment polarities of sentences and documents are calculated ChatGPT from the sentiment score of the constituent words/phrases. Most techniques use the sum of the polarities of words and/or phrases to estimate the polarity of a document or sentence24.

This article assumes some understanding of basic NLP preprocessing and of word vectorisation (specifically tf-idf vectorisation). EHRs, a rich source of secondary health care data, have been widely used to document patients’ historical medical records28. EHRs often contain several different data types, including patients’ profile information, medications, diagnosis history, images.

The rising prevalence of harassment in Middle Eastern countries is mirrored in literary works from the region. However, extracting data from these texts to understand the typology and frequency of the cases poses a significant challenge due to human cognitive limitations and potential biases. Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model.

• Some sentiment analysis tools can also analyze video content and identify expressions by using facial and object recognition technology.
• This helps models understand the meaning of a word based on its surrounding words, leading to better representation of phrases and sentences.
• Provided critical feedback and helped shape the research, analysis, and manuscript.
• It is simple (and often useful) to think of tokens simply as words, but to fine tune your understanding of the specific terminology of NLP tokenization, the Stanford NLP group’s overview is quite useful.

It supports multimedia content by integrating with Speech-to-Text and Vision APIs to analyze audio files and scanned documents. The tool can handle 242 languages, offering detailed sentiment analysis for 218 of them. 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. These tools specialize in monitoring and analyzing sentiment in news content. They use News APIs to mine data and provide insights into how the media portrays a brand or topic.

3. Investigating ChatGPT sentiment labeling

For example, NN is a noun, VD is a verb, JJ is an adjective, and IN is a preposition. The meaningful words, which are verbs, nouns, and adjectives, are intended ChatGPT App to be extracted to reduce the redundancy of words in the text. In this section, the word with a tag that starts with ‘V’, ‘N’, and ‘J’ is extracted.

Representing visually the content of an NLP model or text exploratory analysis is one of the most important tasks in the field of text mining. In many cases, there are some gaps between visualizing unstructured (text) data and structured data. For example, many text visualizations do not represent the text directly, they represent an output of a natural language processing model e.g. word count, character length, word sequences. In 2018, Zalando Research published a state-of-the-art deep learning sequence tagging NLP library called Flair.

Azure AI Language

This pre-trained model can create coherent and structured paragraphs of text given some input. Text classification is the task of categorizing texts into different topics or themes. It can be helpful in various applications such as email classification, semantic analysis nlp topic modeling, and more. Before we start using GPT-4 for NLP tasks, we need to set up our environment with Python and the required libraries. Make sure you have Python 3.7 or higher installed on your local machine, and that it’s running correctly.

The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier. Plotting normalized confusion matrices give some useful insights as to why the accuracies for the embedding-based methods are higher than the simpler feature-based methods like logistic regression and SVM. It is clear that overall accuracy is a very poor metric in multi-class problems with a class imbalance, such as this one — which is why macro F1-scores are needed to truly gauge which classifiers perform better. The logistic regression model classifies a large percentage of true labels 1 and 5 (strongly negative/positive) as belonging to their neighbour classes (2 and 4). Because most of the training samples belonged to classes 2 and 4, it looks like the logistic classifier mostly learned the features that occur in these majority classes. The greater spread (outside the anti-diagonal) for VADER can be attributed to the fact that it only ever assigns very low or very high compound scores to text that has a lot of capitalization, punctuation, repetition and emojis.

To summarize the results obtained in this experiment, the results from CNN-Bi-LSTM achieved better results than those from the other Deep Learning as shown in the Fig. The hyperparameters and the number of tests and training datasets used were the same for each model, even though the results obtained varied. In this study, Keras was used to create, train, store, load, and perform all other necessary operations. Stop words are words that relate to the most common words in a language and do not contribute much sense to a statement; thus, they can be removed without changing the sentence. Furthermore, stemming and lemmatization are the last NLP techniques used on the dataset. The two approaches are used to reduce a derived or inflected word to its root, base, or stem form.

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. Based on the Natural Language Processing Innovation Map, the Tree Map below illustrates the impact of the Top 9 NLP Trends in 2023. Virtual assistants improve customer relationships and worker productivity through smarter assistance functions.

There are many aspects that make Python a great programming language for NLP projects, including its simple syntax and transparent semantics. Developers can also access excellent support channels for integration with other languages and tools. We will use scikit-learn’s implementation of TfidfVectorizer, which converts a collection of raw documents (our twitter dataset) into a matrix of TF-IDF features. Bi-LSTM, the bi-directional version of LSTM, was applied to detect sentiment polarity in47,48,49. A bi-directional LSTM is constructed of a forward LSTM layer and a backward LSTM layer.

In the field of ALSC, Zheng et al. have highlighted the importance of syntactic structures for understanding sentiments related to specific aspects. Their novel neural network model, RepWalk, leverages replicated random walks on syntax graphs to better capture the informative contextual words crucial for sentiment analysis. This method has shown superior performance over existing models on multiple benchmark datasets, underscoring the value of incorporating syntactic structure into sentiment classification representations69. Zhang and Li’s research advances aspect-level sentiment classification by introducing a proximity-weighted convolution network that captures syntactic relationships between aspects and context words. Their model enhances LSTM-derived contexts with syntax-aware weights, effectively distinguishing sentiment for multiple aspects and improving the overall accuracy of sentiment predictions70.

Communication is highly complex, with over 7000 languages spoken across the world, each with its own intricacies. Most current natural language processors focus on the English language and therefore either do not cater to the other markets or are inefficient. The availability of large training datasets in different languages enables the development of NLP models that accurately understand unstructured data in different languages. This improves data accessibility and allows businesses to speed up their translation workflows and increase their brand reach. As we mentioned earlier, to predict the sentiment of a review, we need to calculate its similarity to our negative and positive sets.