Using Watson NLU to help address bias in AI sentiment analysis

Zhang et al. also presented their TransformerRNN with multi-head self-attention149. The usage and development of these BERT-based models prove the potential value of large-scale pre-training models in the application of mental illness detection. Traditional machine learning methods such as support vector machine (SVM), Adaptive Boosting (AdaBoost), Decision Trees, etc. have been used for NLP downstream tasks. Another important feature of this project is the cute little in-text graphics — emojis😄. These graphical symbols have increasingly gained ground in social media communications. According to Emojipedia’s statistics in 2021, a famous emoji reference site, over one-fifth of the tweets now contains emojis (21.54%), while over half of the comments on Instagram include emojis.

Some of the best aspects of PyTorch include its high speed of execution, which it can achieve even when handling heavy graphs. It is also a flexible library, capable of operating on simplified processors or CPUs and GPUs. PyTorch has powerful APIs that enable you to expand on the library, as well as a natural language toolkit. Closing out our list of 10 ChatGPT best Python libraries for NLP is PyTorch, an open-source library created by Facebook’s AI research team in 2016. The name of the library is derived from Torch, which is a deep learning framework written in the Lua programming language. A great option for developers looking to get started with NLP in Python, TextBlob provides a good preparation for NLTK.

Top 15 sentiment analysis tools to consider

To minimize the risks of translation-induced biases or errors, meticulous translation quality evaluation becomes imperative in sentiment analysis. This evaluation entails employing multiple translation tools or engaging multiple human translators to cross-reference translations, thereby facilitating the identification of potential inconsistencies or discrepancies. Additionally, techniques such as back-translation can be employed, whereby the translated text is retranslated back into the original language and compared to the initial text to discern any disparities.

These models can subsequently be employed to classify the sentiment conveyed within the text by incorporating slang, colloquial language, irony, or sarcasm. This facilitates a more accurate determination of the overall sentiment expressed. These graphical representations serve as a valuable resource for understanding how different combinations of translators and sentiment analyzer models influence sentiment analysis performance.

All normal error checking has been removed to keep the main ideas as clear as possible. For SST, the authors decided to focus on movie reviews from Rotten Tomatoes. By scraping movie reviews, they ended up with a total of 10,662 sentences, half of which were negative and the other half positive. After converting all of the text to lowercase and removing non-English sentences, they use the Stanford Parser to split sentences into phrases, ending up with a total of 215,154 phrases. Published in 2013, “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank” presented the Stanford Sentiment Treebank (SST).

Processing unstructured data such as text, images, sound records, and videos are more complicated than processing structured data. The difficulty of capturing semantics and concepts of the language from words proposes challenges to the text processing tasks. A document can not be processed in its raw format, and hence it has to be transformed into a machine-understandable representation27. Selecting the convenient representation scheme suits the application is a substantial step28.

• Our model did not include sarcasm and thus classified sarcastic comments incorrectly.
• With all the complexity necessary for a model to perform well, sentiment analysis is a difficult (and therefore proper) task in NLP.
• Datamation’s focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons.
• It also helps individuals identify problem areas and respond to negative comments10.
• This process involved multiple steps, including tokenization, stop-word removal, and removal of emojis and URLs.
• This achievement marks a pivotal milestone in establishing a multilingual sentiment platform within the financial domain.

Mental illnesses, also called mental health disorders, are highly prevalent worldwide, and have been one of the most serious public health concerns1. According to the latest statistics, millions of people worldwide suffer from one or more mental disorders1. If mental illness is detected at an early stage, it can be beneficial to overall disease progression and treatment. It requires a large amount of data for training, which can be resource-intensive.

CNN and LSTM were compared with the Bi-LSTM using six datasets with light stemming and without stemming. Results emphasized the significant effect of the size and nature of the handled data. The highest performance on large datasets was reached by CNN, whereas the Bi-LSTM achieved the highest performance on small datasets.

Machine translations

Python is widely considered the best programming language, and it is critical for artificial intelligence (AI) and machine learning tasks. Python is an extremely efficient programming language when compared to other mainstream languages, and it is a great choice for beginners thanks to its English-like commands and syntax. Another one of the best aspects of the Python programming language is that it consists of a huge amount of open-source libraries, which make it useful for a wide range of tasks. After you train your sentiment model and the status is available, you can use the Analyze text method to understand both the entities and keywords.

Hybrid approaches combine rule-based and machine-learning techniques and usually result in more accurate sentiment analysis. For example, a brand could train an algorithm on a set of rules and customer reviews, updating the algorithm until it catches nuances specific to the brand or industry. To proficiently identify sentiment within the translated text, a comprehensive consideration of these language-specific features is imperative, necessitating the application of specialized techniques.

Following the presentation of the overall experimental results, the language-specific experimental findings are delineated and discussed in detail below. In the fourth phase of the methodology, we conducted sentiment analysis on the translated data using pre-trained sentiment analysis deep learning models and the proposed ensemble model. The ensemble sentiment analysis model analyzed the text to determine the sentiment polarity (positive, negative, or neutral). The algorithm shows step by step process followed in the sentiment analysis phase. LSTM, Bi-LSTM, GRU, and Bi-GRU were used to predict the sentiment category of Arabic microblogs depending on Emojis features14.

• Next, monitor performance and check if you’re getting the analytics you need to enhance your process.
• A rule-based model involves data labeling, which can be done manually or by using a data annotation tool.
• 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.
• Intent-based analysis can identify the intended action behind a text—for instance, whether a customer wants to seek information, purchase a product, or file a complaint.

The result represents an adapter-BERT model gives a better accuracy of 65% for sentiment analysis and 79% for offensive language identification when compared with other trained models. Sentiment analysis is a Natural Language Processing (NLP) task concerned with opinions, attitudes, emotions, and feelings. It applies NLP techniques for identifying and detecting personal information from opinionated text.

Analyze The Data

Depending on your specific needs, your top picks might look entirely different. IBM Watson is empowered with AI for businesses, and a significant feature of it is natural language, which helps users identify and pick keywords, emotions, segments, and entities. It makes complicated NLP obtainable to company users and enhances team member yield. Below you see the vectors for a hypothetical news article for each group using a bag-of-words approach.

In the second phase of the methodology, the collected data underwent a process of data cleaning and pre-processing to eliminate noise, duplicate content, and irrelevant information. This process involved multiple steps, including tokenization, stop-word removal, and removal of emojis and URLs. Tokenization was performed by dividing the text into individual words or phrases. In contrast, stop-word removal entailed the removal of commonly used words such as “and”, “the”, and “in”, which do not contribute to sentiment analysis.

In addition to the homogenous arrangements composed of one type of deep learning networks, there are hybrid architectures combine different deep learning networks. The hybrid architectures avail from the outstanding characteristic of each network type to empower the model. One of the main advantages of using these models is their high accuracy and performance in sentiment analysis tasks, especially for social media data such as Twitter. These models are pre-trained on large amounts of text data, including social media content, which allows them to capture the nuances and complexities of language used in social media35. Another advantage of using these models is their ability to handle different languages and dialects. The models are trained on multilingual data, which makes them suitable for analyzing sentiment in text written in various languages35,36.

Top Trends in Sentiment Analysis

In addition, bi-directional LSTM and GRU registered slightly more enhanced performance than the one-directional LSTM and GRU. 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. 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. The class labels of offensive language are not offensive, offensive targeted insult individual, offensive untargeted, offensive targeted insult group and offensive targeted insult other.

This suggests that RoBERTa has more parameters than the BERT models, with 123 million features for RoBERTa basic and 354 million for RoBERTa wide30. As BERT uses a different input segmentation, it cannot use GloVe embeddings. GloVe uses simple phrase tokens, whereas BERT separates input into sub—word parts known as word-pieces. In any case, BERT understands its configurable word-piece embeddings along with the overall model. Because they are only common word fragments, they cannot possess its same type of semantics as word2vec or GloVe21. PyTorch is extremely fast in execution, and it can be operated on simplified processors or CPUs and GPUs.

To ensure that the data were ready to be trained by the deep learning models, several NLP techniques were applied. Preprocessing not only reduces the extracted feature space but also improves the classification accuracy40. We picked Stanford CoreNLP for its comprehensive ChatGPT App suite of linguistic analysis tools, which allow for detailed text processing and multilingual support. As an open-source, Java-based library, it’s ideal for developers seeking to perform in-depth linguistic tasks without the need for deep learning models.

Sentiments are then aggregated to determine the overall sentiment of a brand, product, or campaign. To mitigate this concern, incorporating cultural knowledge into the sentiment analysis process is imperative to enhance the accuracy of sentiment identification in translated text. Potential strategies include the utilization of domain-specific lexicons, training data curated for the specific cultural context, or applying machine learning models tailored to accommodate cultural differences.

The process of converting preprocessed textual data to a format that the machine can understand is called word representation or text vectorization. The dataset was collected from various English News YouTube channels, such as CNN, Aljazeera, WION, BBC, and Reuters. We obtained a dataset from YouTube; we selected the popular channels and videos related to the Hamas-Israel war that had indicated dataset semantic relevance.

The results of channel 2 & channel 3 are flattened and stored into flat 2 & flat three layers consecutively. The output stored in flat 1, flat 2 & flat three is finally concatenated and stored in the merged layer. After getting the output from the merged layer, two dense layers have been used.

Moreover, it helps maintain data privacy and protects sensitive information by identifying and redacting Personally Identifiable Information (PII). Add labels to messages manually or use the Inbox Assistant to automatically go through your messages and label all relevant items that contain the specified keywords. Sentiment analysis is a transformative tool in the realm of chatbot interactions, enabling more nuanced and responsive communication. By analyzing the emotional tone behind user inputs, chatbots can tailor their responses to better align with the user’s mood and intentions.

Regarding how to incorporate the emojis specifically, the methods didn’t show a significant difference, so a straightforward way — directly treating the emojis as regular word tokens — would do the job perfectly. Yet, considering that half of the common BERT-based encoders in our study don’t support emojis, we recommend using the emoji2desc method. That means converting emojis to their official textual description using a simple line of code I mentioned before, which can easily handle the out-of-vocabulary emoji tokens. The best model to handle SMSA tasks and coordinate with emojis is the Twitter-RoBERTa encoder!

That means you will make fewer mistakes as you react to a rapidly changing world. In the bottom-up approach, For cross-validation, the adoption of NLP in finance solutions & services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation. The adoption of NLP in the finance industry has been driven by the increasing demand for automated and efficient financial services worldwide.

How to use sentiment analysis

Assuming you are analyzing a text resource, start by removing unnecessary punctuation, characters, and other cleaning text. Spending time on this step will improve the quality of the resulting analysis. The application we will be building is a real-time chat application that is able to detect the tone of the users’ messages. As you can imagine the use cases for this can span greatly, from understanding customers’ interaction with customer service chats to understanding how well a production AI chatbot is performing.

Many large companies are overwhelmed by the number of requests with varied topics. NLP and natural language understanding (NLU) can detect the emotion and tone behind the written or spoken word, helping companies understand the urgency of specific requests and support tickets. Classification also plays a role in sentiment analysis and is sentiment analysis nlp can be used to sort requests to the proper channels or departments. One of the pre-trained models is a sentiment analysis model trained on an IMDB dataset, and it’s simple to load and make predictions. While it is a useful pre-trained model, the data it is trained on might not generalize as well as other domains, such as Twitter.

This scenario, simple though it may seem, shows how effectively sentiment analysis can improve customer outcomes. You can foun additiona information about ai customer service and artificial intelligence and NLP. It’s an example of augmented intelligence, where the NLP assists human performance. In this case, the customer service representative partners with machine learning software in pursuit of a more empathetic exchange with another person. Logistic regression predicts 1568 correctly identified negative comments in sentiment analysis and 2489 correctly identified positive comments in offensive language identification.

It has an easy-to-use interface that enables beginners to quickly learn basic NLP applications like sentiment analysis and noun phrase extraction. A dedication to trust, transparency, and explainability permeate IBM Watson. Data scientists and SMEs must build dictionaries of words that are somewhat synonymous with the term interpreted with a bias to reduce bias in sentiment analysis capabilities. Sentiment analysis is a vital component in customer relations and customer experience. Several versatile sentiment analysis software tools are available to fill this growing need. Sentiment analysis tools are essential to detect and understand customer feelings.

Miramant is a popular speaker, futurist, and a strategic business & technology advisor to enterprise companies and startups. In 2020, we’ve all started to learn the value of large scale public health data analysis due to the rapid spread of COVID. In these crises, detecting changes in social behavior quickly is essential. For example, a recent project analyzed over 1,000 tweets using the keyword masks to understand how people are thinking and feeling about masks. In the rest of this post, I will qualitatively analyze a couple of reviews from the high complexity group to support my claim that sentiment analysis is a complicated intellectual task, even for the human brain. Traditional classification models cannot differentiate between these two groups, but our approach provides this extra information.

In the above gist, you can see upon a client sending a new message, the server will call 2 functions, getTone and updateSentiment, while passing in the text value of the chat message into those functions. 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. The plot below shows how these two groups of reviews are distributed on the PSS-NSS plane.

This score seems to be more reliable because it encompasses the overall sentiment of this corpus. But we can see from the scores above that tweets that have been classified as Hate Speech are especially negative. 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.

There are other types of texts written for specific experiments, as well as narrative texts that are not published on social media platforms, which we classify as narrative writing. For example, in one study, children were asked to write a story about a time that they had a problem or fought with other people, where researchers then analyzed their personal narrative to detect ASD43. In addition, a case study on Greek poetry of the 20th century was carried out for predicting suicidal tendencies44.

The experiments conducted in this study focus on both English and Turkish datasets, encompassing movie and product reviews. The classification task involves two-class polarity detection (positive-negative), with the neutral class excluded. Encouraging outcomes are achieved in polarity detection experiments, notably by utilizing general-purpose classifiers trained on translated corpora. However, it is underscored that the discrepancies between corpora in different languages warrant further investigation to facilitate more seamless resource integration. NLP is a branch of artificial intelligence (AI) that combines computational linguistics with statistical and machine learning models, enabling computers to understand human language.

For many text mining tasks including text classification, clustering, indexing, and more, stemming helps improve accuracy by shrinking the dimensionality of machine learning algorithms and grouping words according to concept. In this way, stemming serves as an important step in developing large language models. Our model did not include sarcasm and thus classified sarcastic comments incorrectly.