Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning Scientific Reports

Stacked LSTM layers produced feature representations more appropriate for class discrimination. The results highlighted that the model realized the highest performance on the largest considered dataset. The online Arabic SA system Mazajak was developed based on a hybrid architecture of CNN and LSTM46. The applied word2vec word embedding was trained on a large and diverse dataset to cover several dialectal Arabic styles. 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.

• 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.
• This is particularly emblematic in sentence 1, where specialists should have recognized that although the sentiment was positive for Glencore, the target company was Barclays, which just wrote the report.
• To obtain a length n vector from a convolution layer, a 1-max pooling function is employed per feature map.

As we mentioned earlier, to predict the sentiment of a review, we need to calculate its similarity to our negative and positive sets. We will call these similarities negative semantic scores (NSS) and positive semantic scores (PSS), respectively. There are several ways to calculate the similarity between two collections of words. One of the most common approaches is to build the document vector by averaging over the document’s wordvectors.

Text summarization, semantic search, and multilingual language models expand the use cases of NLP into academics, content creation, and so on. The cost and resource-efficient development of NLP solutions is also a necessary requirement to increase their adoption. The startup’s summarization solution, DeepDelve, uses NLP to provide accurate and contextual answers to questions based on information from enterprise documents. Additionally, it supports search filters, multi-format documents, autocompletion, and voice search to assist employees in finding information. The startup’s other product, IntelliFAQ, finds answers quickly for frequently asked questions and features continuous learning to improve its results.

Why Do You Need Sentiment Analysis and NLP in Social Media?

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. The methods and detection sets refer to NLP methods used for mental illness identification. Finding the right data, applying algorithms to that data, and getting usable business insights isn’t easy. After all, large companies with deep resources have made mistakes in their natural language processing projects. Contact Blue Orange Digital today to find out how you can get faster insights from social media and other data in your organization. The three key technologies gaining a foothold in the NLP in Finance market are machine learning, deep Learning, and natural language generation.

The exhibited performace is a consequent on the fact that the unseen dataset belongs to a domain already included in the mixed dataset. Also, all terms in the corpus are encoded, including stop words and Arabic words composed in English characters ChatGPT that are commonly removed in the preprocessing stage. The elimination of such observations may influence the understanding of the context. Each word is assigned a continuous vector that belongs to a low-dimensional vector space.

• NLP allows users to dig into unstructured data to get instantly actionable insights.
• Besides, the common CNN-LSTM combination applied for Arabic SA used only one convolutional layer and one LSTM layer.
• As it is well known, a sentence is made up of various parts of speech (POS), and each combination yields a different accuracy rate.
• This research contributes to developing a state-of-the-art Arabic sentiment analysis system, creating a new dialectal Arabic sentiment lexicon, and establishing the first Arabic-English parallel corpus.

The TorchText library contains hundreds of useful classes and functions for dealing with natural language problems. The demo program uses TorchText version 0.9 which has many major changes from versions 0.8 and earlier. After you download the whl file, you can install TorchText by opening a shell, navigating to the directory containing the whl file, and issuing the command “pip install (whl file).” All architectures employ a character embedding layer to convert encoded text entries to a vector representation.

Amharic political sentiment analysis using deep learning approaches

When such malformed stems escape the algorithm, the Lovins stemmer can reduce semantically unrelated words to the same stem—for example, the, these, and this all reduce to th. Of course, these three words are all demonstratives, and so share a grammatical function. This means the Lovins generated stems do not properly represent word groups.

It offers text classification, text summarization, embedding, sentiment analysis, sentence similarity, and entailment services. The amount of datasets in English dominates (81%), followed by datasets in Chinese (10%), Arabic (1.5%). The trend of the number of articles containing machine learning-based and deep learning-based methods for detecting mental illness from 2012 to 2021. Even organizations with large budgets like national governments and global corporations are using data analysis tools, algorithms, and natural language processing. Despite these challenges, the market opportunity for NLP in the finance industry remains significant. The development of customized NLP solutions & services for specific financial use cases is a major market opportunity.

To find the class probabilities we take a softmax across the unnormalized scores. The class with the highest class probabilities is taken to be the predicted class. The id2label attribute which we stored in the model’s configuration earlier on can be used to map the class id (0-4) to the class labels (1 star, 2 stars..). The DataLoader initializes a pretrained tokenizer and encodes the input sentences.

Azure AI Language offers free 5,000 text records per month and costs $25 per 1,000 succeeding text records. Before collecting data, define your goals for what you want to learn through sentiment analysis. Sentiment analysis uses computational techniques to determine the emotions and attitudes within textual data. Natural language processing (NLP) and machine learning (ML) are two of the major approaches that are used. Fine-grained analysis delves deeper than classifying text as positive, negative, or neutral, breaking down sentiment indicators into more precise categories.

Results reported that Bi-GRU outperformed Bi-LSTM with slightly different performance on a small dataset of short dialectical Arabic tweets. Experiments evaluated diverse methods of combining the bi-directional features and stated that concatenation led to the best performance for LSTM and GRU. Besides, the detection of religious hate speech was analyzed as a classification is sentiment analysis nlp task applying a GRU model and pre-trained word embedding50. The embedding was pre-trained on a Twitter corpus that contained different Arabic dialects. Supporting the GRU model with handcrafted features about time, content, and user boosted the recall measure. This process requires training a machine learning model and validating, deploying and monitoring performance.

These tools have been around for over a decade, and they are getting better every year. Every indicator suggests that we will see more data produced over time, not less. In the primary research process, various sources from both supply and demand sides were interviewed to obtain qualitative & quantitative information on the market.

The N-words sequences extracted from the corpus are employed as enriching features. But, the number of words selected for effectively representing a document is difficult to determine27. The main drawback of BONG is more sparsity and higher dimensionality compared to BOW29. Bag-Of-Concepts is another document representation approach where every dimension is related to a general concept described by one or multiple words29. Training and validation accuracy and loss values for offensive language identification using adapter-BERT. Although RoBERTa’s architecture is essentially identical to that of BERT, it was designed to enhance BERT’s performance.

One of the reasons Polyglot is so useful for NLP is that it supports extensive multilingual applications. You can foun additiona information about ai customer service and artificial intelligence and NLP. Its documentation shows that it supports tokenization for 165 languages, language detection for 196 languages, and part-of-speech tagging for 16 languages. Each one of the segregated modules and packages is useful for standard and advanced NLP tasks. Some of these tasks include extraction of n-grams, frequency lists, and building a simple or complex language model. There are many aspects that make Python a great programming language for NLP projects, including its simple syntax and transparent semantics.

As I promised in the introduction, now I will show how this model will provide additional valuable information that supervised models are not providing. Namely, I will show that this model can give us an understanding of the sentiment complexity of the text. In addition to the fact that both scores are normally distributed, their values correlate with the review’s length.

Moreover, social media’s explosive growth in the last decade has provided a vast amount of data for users to mine, providing insights into their thoughts and emotions17. Social media platforms provide valuable insights into public attitudes, particularly on war-related issues, aiding in conflict resolution efforts18. Despite their precision and time-consuming nature, machine-learning algorithms are the foundation of sentiment analysis16. One common and effective type of sentiment classification algorithm is support vector machines. If your company doesn’t have the budget or team to set up your own sentiment analysis solution, third-party tools like Idiomatic provide pre-trained models you can tweak to match your data.

It’s priced based on the NLU item, equivalent to one text unit or up to 10,000 characters. A rule-based model involves data labeling, which can be done manually or by ChatGPT App using a data annotation tool. A machine learning model can be built by training a vast amount of data to analyze text to give more accurate and automated results.

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. Natural language processing (NLP) is a subset of AI which finds growing importance due to the increasing amount of unstructured language data.

Learning takes place by updating the parameters and repeating the process until your cost is minimised. The standard CNN structure is composed of a convolutional layer and a pooling layer, followed by a fully-connected layer. Some studies122,123,124,125,126,127 utilized standard CNN to construct classification models, and combined other features such as LIWC, TF-IDF, BOW, and POS. In order to capture sentiment information, Rao et al. proposed a hierarchical MGL-CNN model based on CNN128. Lin et al. designed a CNN framework combined with a graph model to leverage tweet content and social interaction information129.

Users interacting with chatbots may not even realize they are not talking to a person. Chatbots have become more content-sensitive and can offer a better user experience to customers. When you link NLP with your data, you can assess customer feedback to know which customers have issues with your product. You can also optimize processes and free your employees from repetitive jobs. When doing repetitive tasks, like reading or assessing survey responses, humans can make mistakes that hamper results. NLP tools are trained to the language and type of your business, customized to your requirements, and set up for accurate analysis.

Employee sentiment analysis requires a comprehensive strategy for mining these opinions — transforming survey data into meaningful insights. Employee sentiment analysis can make an organization aware of its strengths and weaknesses by gauging its employees. This can provide organizations with insight into positive and negative feelings workers hold toward the organization, its policies and the workplace culture. Sprout Social’s Tagging feature is another prime example of how NLP enables AI marketing.

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. 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. The unstructured nature of YouTube comments, the use of colloquial language, and the expression of a wide range of opinions and emotions present challenges for this task. Since the correlation between the front and back of a sequence cannot be described, traditional machine learning is ineffective in handling sequence learning.

The interdisciplinary field combines techniques from the fields of linguistics and computer science, which is used to create technologies like chatbots and digital assistants. Depending on how you design your sentiment model’s neural network, it can perceive one example as a positive statement and a second as a negative statement. Customer service platforms integrate with the customer relationship management (CRM) system. This integration enables a customer service agent to have the following information at their fingertips when the sentiment analysis tool flags an issue as high priority. Here are five sentiment analysis tools that demonstrate how different options are better suited for particular application scenarios. Customer interactions with organizations aren’t the only source of this expressive text.

This activity can result in more focused, empathetic responses to customers. Morphological diversity of the same Arabic word within different contexts was considered in a SA task by utilizing three types of feature representation44. Character, Character N-Gram, and word features were employed for an integrated CNN-LSTM model. The fine-grained character features enabled the model to capture more attributes from short text as tweets. The integrated model achieved an enhanced accuracy on the three datasets used for performance evaluation. Moreover, a hybrid dataset corpus was used to study Arabic SA using a hybrid architecture of one CNN layer, two LSTM layers and an SVM classifier45.

A sentiment analysis model can not notice this sentiment shift if it did not learn how to use contextual indications to predict sentiment intended by the author. To illustrate this point, let’s see review #46798, which has a minimum S3 in the high complexity group. Starting with the word “Wow” which is the exclamation of surprise, often used to express astonishment or admiration, the review seems to be positive. But the model successfully captured the negative sentiment expressed with irony and sarcasm.

Its extensive model hub provides access to thousands of community-contributed models, including those fine-tuned for specific use cases like sentiment analysis and question answering. Hugging Face also supports integration with the popular TensorFlow and PyTorch frameworks, bringing even more flexibility to building and deploying custom models. Azure AI Language lets you build natural language processing applications with minimal machine learning expertise. Pinpoint key terms, analyze sentiment, summarize text and develop conversational interfaces. It leverages natural language processing (NLP) to understand the context behind social media posts, reviews and feedback—much like a human but at a much faster rate and larger scale.

All of these issues imply a learning curve to properly use the (biased) API. Sometimes I had to do many trials until I reached the desired outcome with minimal consistency. Bidirectional encoder representations from rransformers (BERT) representation. Table 1 summarises several relevant articles and research papers on review analysis. To understand how, here is a breakdown of key steps involved in the process.

Another business might be interested in combining this sentiment data to guide future product development, and would choose a different sentiment analysis tool. The NLP machine learning model generates an algorithm that performs sentiment analysis of the text from the customer’s email or chat session. Business rules related to this emotional state set the customer service agent up for the appropriate response. In this case, immediate upgrade of the support request to highest priority and prompts for a customer service representative to make immediate direct contact. The demo program uses a neural network architecture that has an EmbeddingBag layer, which is explained shortly.

Sentiment analysis and natural language processing (NLP) of social media is a proven way to draw insight from people and society. Instead of asking an analyst to spend weeks reading social media comments and providing a report, sentiment analysis can give you a quick summary. In the secondary research process, various sources were referred for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendor websites.

For example, in several of my NLP projects I wanted to retain the word “don’t” rather than split it into three separate tokens. One approach to create a custom tokenizer is to refactor the TorchText basic_english tokenizer source code. The MyTokenizer class constructs a regular expression and the tokenize() method applies the regular expression to its input text. The demo program concludes by predicting the sentiment for a new review of, “Overall, I liked the film.” The prediction is in the form of two pseudo-probabilities with values [0.3766, 0.6234].

It can extract critical information from unstructured text, such as entities, keywords, sentiment, and categories, and identify relationships between concepts for deeper context. SpaCy stands out for its speed and efficiency in text processing, making it a top choice for large-scale NLP tasks. 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. I was able to repurpose the use of zero-shot classification models for sentiment analysis by supplying emotions as labels to classify anticipation, anger, disgust, fear, joy, and trust.