Character gated recurrent neural networks for Arabic sentiment analysis Scientific Reports

There is a widespread belief that neutral texts provide less guidance than those that make overtly positive or negative statements. In order to achieve the common aim of automation within the research community, adequate scientific literature understanding is essential. It has been calculated that 8–9% of the total research volume generated each year is increasing. An overabundance of knowledge leads to the ‘reinventing the wheel’ syndrome, which has an impact on the literature review process.

Bi-GRU-CNN hybrid models registered the highest accuracy for the hybrid and BRAD datasets. On the other hand, the Bi-LSTM and LSTM-CNN models wrote the lowest performance for the hybrid and BRAD datasets. The proposed Bi-GRU-CNN model reported 89.67% accuracy for the mixed dataset and nearly 2% enhanced accuracy for the BRAD corpus. In the Arabic language, the character form changes according to its location in the word.

Predict

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. NLP is a branch of artificial intelligence (AI) that combines computational linguistics with statistical and machine learning models, enabling computers to understand human language.

• MonkeyLearn also connects easily to apps and BI tools using SQL, API and native integrations.
• Other common Python language tokenizers are in the spaCy library and the NLTK (natural language toolkit) library.
• These graphical representations serve as a valuable resource for understanding how different combinations of translators and sentiment analyzer models influence sentiment analysis performance.
• The deep learning segment is projected to witness a higher growth rate during the forecast period.

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.

How Proper Sentiment Analysis Is Achieved

Primary research also helped understand various trends related to technologies, applications, deployments, and regions. In the primary research process, various sources from both supply and demand sides were interviewed to obtain qualitative & quantitative information on the market. The research study for the NLP in finance market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, ChatGPT App preferred NLP in finance providers, third-party service providers, consulting service providers, end-users, and other commercial enterprises. In-depth interviews were conducted with primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative & quantitative information and assess the market’s prospects. One of the primary reasons for the difficulty in managing large volumes of unstructured data is the lack of standardization.

Confusion matrix of Bi-LSTM for sentiment analysis and offensive language identification. Confusion matrix of CNN for sentiment analysis and offensive language identification. Logistic regression is a classification technique and it is far more straightforward to apply than other approaches, specifically in the area of machine learning. The accuracy of the LSTM based architectures versus the GRU based architectures is illastrated in Fig. Results show that GRUs are more powerful to disclose features from the rich hybrid dataset.

Identifying bias in sentiment analysis

Your data can be in any form, as long as there is a text column where each row contains a string of text. To follow along with this example, you can read in the Reddit depression dataset here. This dataset is made available under the Public Domain Dedication and License v1.0.

• Taking this into account, we suggested using deep learning algorithms to find YouTube comments about the Palestine-Israel War, since the findings will help Palestine and Israel find a peaceful solution to their conflict.
• Combinations of word embedding and weighting approaches were investigated for sentiment analysis of product reviews52.
• This shows that both corpuses are similar, but the Hate Speech label has slightly more negative tweets, on average.
• Using progressively more and more complex models, we were able to push up the accuracy and macro-average F1 scores to around 48%, which is not too bad!

Goally used this capability to monitor social engagement across their social channels to gain a better understanding of their customers’ complex needs. Natural language processing powers content suggestions by enabling ML models to contextually understand and generate human language. NLP uses NLU to analyze and interpret data while NLG generates personalized and relevant content recommendations to users. Its ability to understand the intricacies of human language, including context and cultural nuances, makes it an integral part of AI business intelligence tools. 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.

Medallia’s experience management platform offers powerful listening features that can pinpoint sentiment in text, speech and even video. InMoment is a customer experience platform that uses Lexalytics’ AI to analyze text from multiple sources and translate it into meaningful insights. Continuous updates ensure the hybrid model improves over time, enhancing its ability to accurately reflect customer opinions. Select the type of data suitable for your project or research and determine your data collection strategy.

Figure 10b represents the graph of model loss when the Glove plus LSTM model is applied. The blue line represents training loss & the orange line represents validation loss. Figure 10(c) shows ChatGPT the confusion matrix formed by the Glove plus LSTM model. The total positively predicted samples, which are already positive out of 27,727, are 17,940 & negative predicted samples are 3075.

As a result, we used deep learning techniques to design and develop a YouTube user sentiment analysis of the Hamas-Israel war. Therefore, we collected comments about the Hamas-Israel conflict from YouTube News channels. Next, significant NLP preprocessing operations are carried out to enhance our classification model and carry out an experiment on DL algorithms.

A necessary first step for companies is to have the sentiment analysis tools in place and a clear direction for how they aim to use them. When the organization determines how to detect positive and negative sentiment in customer expressions, it what is sentiment analysis in nlp can improve its interactions with the customer. By exploring historical data on customer interaction and experience, the company can predict future customer actions and behaviors, and work toward making those actions and behaviors positive.

Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content. Sentiment analysis can track changes in attitudes towards companies, products, or services, or individual features of those products or services. Natural language processing tools use algorithms and linguistic rules to analyze and interpret human language. NLP tools can extract meanings, sentiments, and patterns from text data and can be used for language translation, chatbots, and text summarization tasks. We chose Google Cloud Natural Language API for its ability to efficiently extract insights from large volumes of text data. You can foun additiona information about ai customer service and artificial intelligence and NLP. Its integration with Google Cloud services and support for custom machine learning models make it suitable for businesses needing scalable, multilingual text analysis, though costs can add up quickly for high-volume tasks.

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

On the other hand, LSTMs are more sensitive to the nature and size of the manipulated data. Stacking multiple layers of CNN after the LSTM, GRU, Bi-GRU, and Bi-LSTM reduced the number of parameters and boosted the performance. The character vocabulary includes all characters found in the dataset (Arabic characters, , Arabic numbers, English characters, English numbers, emoji, emoticons, and special symbols).

In this study, research stages include feature selection, feature expansion, preprocessing, and balancing with SMOTE. The highest accuracy value was obtained on the CNN-GRU model with an accuracy value of 95.69% value. Moreover, the LSTM neurons are split into two directions, one for forward states and the other for backward states, to form bidirectional LSTM networks32.

Some authors recently explored with code-mixed language to identify sentiments and offensive contents in the text. Similar results were obtained using ULMFiT trained on all four datasets, with TRAI scoring the highest at 70%. For the identical assignment, BERT trained on TRAI received a competitive score of 69%.

To make the framework consistent, a score method and a predict method are included with each new sentiment classifier, as shown below. The score method outputs a unique sentiment class for a text sample, and the predict method applies the score method to every sample in the test dataset to output a new column, ‘pred’ in the test DataFrame. It is then trivial to compute the model’s accuracy and F1-scores by using the accuracy method defined in the Base class. 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.

Take the time to research and evaluate different options to find the right fit for your organization. Ultimately, the success of your AI strategy will greatly depend on your NLP solution. Google Cloud Natural Language API is widely used by organizations leveraging Google’s cloud infrastructure for seamless integration with other Google services. It allows users to build custom ML models using AutoML Natural Language, a tool designed to create high-quality models without requiring extensive knowledge in machine learning, using Google’s NLP technology. Read eWeek’s guide to the best large language models to gain a deeper understanding of how LLMs can serve your business.

Each day, we are challenged with texts containing a wide range of insults and harsh language. Automatic intelligent software that detects flames or other offensive words would be beneficial and could save users time and effort. These works defy language conventions by being written in a spoken style, which makes them casual. Because of the expanding volume of data and regular users, the NLP has recently focused on understanding social media content2. Deep neural architectures have proved to be efficient feature learners, but they rely on intensive computations and large datasets. In the proposed work, LSTM, GRU, Bi-LSTM, Bi-GRU, and CNN were investigated in Arabic sentiment polarity detection.