Using Watson NLU to help address bias in AI sentiment analysis

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.

A great option for developers looking to get started with NLP in Python, TextBlob provides a good preparation for NLTK. It has an easy-to-use interface that enables beginners to quickly learn basic NLP applications like sentiment analysis and noun phrase extraction. Stanford CoreNLP is a library consisting of a variety of human language technology tools that help with the application of linguistic analysis tools to a piece of text. CoreNLP enables you to extract a wide range of text properties, such as named-entity recognition, part-of-speech tagging, and more with just a few lines of code. Natural language processing, or NLP, is a field of AI that aims to understand the semantics and connotations of natural human languages.

The ablation study results reveal several important insights about the contributions of various components to the performance of our model. This underscores the synergy between the components, suggesting that each plays a crucial role in the model’s ability to effectively process and analyze linguistic data. Particularly, the removal of the refinement process results in a uniform decrease in performance across all model variations and datasets, albeit relatively slight. This suggests that while the refinement process significantly enhances the model’s accuracy, its contribution is subtle, enhancing the final stages of the model’s predictions by refining and fine-tuning the representations.

Additionally, incorporating more varied samples can help mitigate the sensitivity caused by high-frequency words. Furthermore, it is important to consider the limitations of training models in a specific context, such as sexual harassment in Middle East countries. Models trained on such data may not perform as expected when applied to datasets from different contexts, such as anglophone literature from another region. This discrepancy arises due to cultural and social differences that influence language usage and interpretation. To enhance model performance across different contexts, it is advisable to train models on datasets that encompass a broader range of cultural backgrounds and social interactions.

Boosting is trained by ensemble learning, where the weight of the data point changes based on the previous performance. Bagging algorithm generated a sub-sample from the training set and trained different models, and the prediction was the most voted among the trained models. The limitations of Boosting and Bagging are the computational expensive and lack of interpretability. Logistic regression is a statistical model based on a decision boundary to predict the probability of labels. Naïve Bayes classification is popular in document categorization and information retrieval. This model used the frequency of the words in the document and based on Bayes theorem to predict the probability of the models.

The training process is designed to learn embeddings that effectively capture the semantic relationships between words. Meltwater’s AI-powered tools help you monitor trends and public opinion about your brand. Their sentiment analysis feature breaks down the tone of news content into positive, negative or neutral using deep-learning technology. SpaCy is a general-purpose NLP library that provides a wide range of features, including tokenization, lemmatization, part-of-speech tagging, named entity recognition, and sentiment analysis. SpaCy is also relatively efficient, making it a good choice for tasks where performance and scalability are important. Sentiment analysis lets you understand how your customers really feel about your brand, including their expectations, what they love, and their reasons for frequenting your business.

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 ChatGPT App 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).

Best Python Libraries for Sentiment Analysis

Advances in learning models, such as reinforced and transfer learning, are reducing the time to train natural language processors. Besides, sentiment analysis and semantic search enable language processors to better understand text and speech context. Named entity recognition (NER) works to identify names and persons within unstructured data while text summarization reduces text volume to provide important key points. Language transformers are also advancing language processors through self-attention.

This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. While AI, ML, deep learning and neural networks are related technologies, the terms are often used interchangeably. You can foun additiona information about ai customer service and artificial intelligence and NLP. Transformers have become the backbone of various state-of-the-art models in NLP, including BERT, GPT and T5 (Text-to-Text Transfer Transformer), among others. They excel in tasks such as language modeling, machine translation, text generation and question answering.

This method capitalizes on large-scale data availability to create robust and effective sentiment analysis models. By training models directly on target language data, the need for translation is obviated, enabling more efficient sentiment analysis, especially in scenarios where translation feasibility or practicality is a concern. Text mining collects and analyzes structured and unstructured content in documents, social media, comments, newsfeed, databases, and repositories. The use case can leverage on text analytics solution for crawling and importing content, parsing and analyzing content, and creating a searchable index. Semantic analysis describes the process of understanding natural language–the way that humans communicate–based on meaning and context.

Original Imbalanced Data

This technology is super impressive and is quickly proving how valuable it can be in our daily lives, from making reservations for us to eliminating the need for human powered call centers. Twitter has been growing in popularity and nowadays, it is used every day by people to express opinions about different topics, such as products, movies, music, politicians, semantic analysis nlp events, social events, among others. A lot of movies are released every year, but if you are a Marvel’s fan like I am, you’d probably be impatient to finally watch the new Avengers movie. Feel free to leave any feedback (positive or constructive) in the comments, especially about the math section, since I found that the most challenging to articulate.

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. 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.

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.

Phrase structure rules form the core of constituency grammars, because they talk about syntax and rules that govern the hierarchy and ordering of the various constituents in the sentences. The preceding output gives a good sense of structure after shallow parsing the news headline. You can see that the semantics of the words are not affected by this, yet our text is still standardized. Lemmatization is very similar to stemming, where we remove word affixes to get to the base form of a word. However, the base form in this case is known as the root word, but not the root stem.

Another top application for TextBlob is translations, which is impressive given the complex nature of it. With that said, TextBlob inherits low performance form NLTK, and it shouldn’t be used for large scale production. Say now we’d like to compare the performance of two of our better models to keep fine-tuning. Simply select two experiments from your list and click the Diff button and Comet will allow you to visually inspect every code and hyperparameter change, as well as side-by-side visualizations of both experiments. The first draft of the manuscript was written by [E.O.] and all authors commented on previous versions of the manuscript. Histogram and density plot of the numeric value of the compound sentiment by sexual offence types.

Similarly, GPT-3 paired with both LibreTranslate and Google Translate consistently shows competitive recall scores across all languages. For Arabic, the recall scores are notably high across various combinations, indicating effective sentiment analysis for this language. These findings suggest that the proposed ensemble model, along with GPT-3, holds promise for improving recall in multilingual sentiment analysis tasks across diverse linguistic contexts. This enhances the model’s ability to identify a wide range of syntactic features in the given text, allowing it to surpass the performance of classical word embedding models.

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.

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.

Frequently Asked Questions (FAQs) about GPT-4 for Natural Language Processing (NLP)

The three layers Bi-LSTM model trained with the trigrams of inverse gravity moment weighted embedding realized the best performance. A hybrid parallel model that utlized three seprate channels was proposed in51. Character CNN, word CNN, and sentence Bi-LSTM-CNN channels were trained parallel. A positioning binary embedding scheme (PBES) was proposed to formulate contextualized embeddings that efficiently represent character, word, and sentence features. The model performance was more evaluated using the IMDB movie review dataset. Experimental results showed that the model outperformed the baselines for all datasets.

Sentiment analysis tools are essential to detect and understand customer feelings. Companies that use these tools to understand how customers feel can use it to improve CX. Companies can use customer sentiment to alert service representatives when the customer is upset and enable them to reprioritize the issue and respond with empathy, as described in the customer service use case. When harvesting social media data, companies should observe what comparisons customers make between the new product or service and its competitors to measure feature-by-feature what makes it better than its peers.

Skip-Gram follows a reversed strategy as it predicts the context words based on the centre word. GloVe uses the vocabulary words co-occurrence matrix as input to the learning algorithm where each matrix cell holds the number of times by which two words occur in the same context. A discriminant feature of word embedding is that they capture semantic and syntactic connections among words. Embedding vectors of semantically similar or syntactically similar words are close vectors with high similarity29. In addition to gated RNNs, Convolutional Neural Network (CNN) is another common DL architecture used for feature detection in different NLP tasks.

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.

When the organization determines how to detect positive and negative sentiment in customer expressions, it 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. Topic clustering through NLP aids AI tools in identifying semantically similar words and contextually understanding them so they can be clustered into topics. This capability ChatGPT provides marketers with key insights to influence product strategies and elevate brand satisfaction through AI customer service. Natural language understanding (NLU) enables unstructured data to be restructured in a way that enables a machine to understand and analyze it for meaning. Deep learning enables NLU to categorize information at a granular level from terabytes of data to discover key facts and deduce characteristics of entities such as brands, famous people and locations found within the text.

IBM Watson Natural Language Understanding stands out for its advanced text analytics capabilities, making it an excellent choice for enterprises needing deep, industry-specific data insights. Its numerous customization options and integration with IBM’s cloud services offer a powerful and scalable solution for text analysis. The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request. \(C\_correct\) represents the count of correctly classified sentences, and \(C\_total\) denotes the total number of sentences analyzed. Genism is a bespoke Python library that has been designed to deliver document indexing, topic modeling and retrieval solutions, using a large number of Corpora resources. This means it can process an input that exceeds the available RAM on a system.

Providing such data is an expensive and time-consuming process that is not possible or readily accessible in many cases. Additionally, the output of such models is a number implying how similar the text is to the positive examples we provided during the training and does not consider nuances such as sentiment complexity of the text. Sentiment analysis is an application of natural language processing (NLP) that reveals the emotional states in human speech or text — in this case, the speech and text that customers generate. Businesses can use machine-learning-based sentiment analysis software to examine this speech and text for positive or negative sentiment about the brand.

• Starting with the word “Wow” which is the exclamation of surprise, often used to express astonishment or admiration, the review seems to be positive.
• Python is a high-level programming language that supports dynamic semantics, object-oriented programming, and interpreter functionality.
• In a dynamic digital age where conversations about brands and products unfold in real-time, understanding and engaging with your audience is key to remaining relevant.
• The Google Translate ensemble model garners the highest overall accuracy (86.71%) and precision (80.91%), highlighting its potential for robust sentiment analysis tasks.

Released to the public by Stanford University, this dataset is a collection of 50,000 reviews from IMDB that contains an even number of positive and negative reviews with no more than 30 reviews per movie. As noted in the dataset introduction notes, “a negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. Neutral reviews are not included in the dataset.” In my previous project, I split the data into three; training, validation, test, and all the parameter tuning was done with reserved validation set and finally applied the model to the test set. Considering that I had more than 1 million data for training, this kind of validation set approach was acceptable. But this time, the data I have is much smaller (around 40,000 tweets), and by leaving out validation set from the data we might leave out interesting information about data.

These are usually words that end up having the maximum frequency if you do a simple term or word frequency in a corpus. Unstructured data, especially text, images and videos contain a wealth of information. Customer service platforms integrate with the customer relationship management (CRM) system.

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.

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.