Welcome AI Ethics Enthusiasts!
In this post, we will dive into a fascinating topic: Natural Language Processing (NLP) for Emotion Recognition through Sentiment Analysis.
Understanding Sentiment Analysis
NLP is an exciting field involving AI techniques to understand and analyse human language. It has various applications, including language translation, speech recognition, text summarisation, and sentiment analysis.
When it comes to understanding emotions, sentiment analysis is key. Sentiment analysis focuses on identifying and categorising emotions expressed in text, such as social media posts, customer reviews, and news articles. It classifies sentiments into categories like positive, negative, or neutral. On the other hand, if we want to determine someone's inclination towards a specific policy, party, or issue, we use Stance Detection, which involves classes like favour and against. Using NLP algorithms, sentiment analysis can capture the underlying emotional tone or valence and subjective opinions conveyed in written content.
However, accurately analysing sentiments in text is a significant challenge. Language is complex, and many factors can impact sentiment recognition, such as dialects, colloquialisms, irony, and cynicism.
Big Challenges Ahead
Let's explore some examples to understand how dialects and colloquialisms affect sentiment analysis. When it comes to dialects, different regions have their own dialects, which can vary in vocabulary, grammar, and pronunciation. For example, in Ireland, you might hear “What’s the craic?” as a greeting equivalent to “What’s new?” in American English. On the other hand, colloquialisms are informal expressions or phrases that are commonly used in specific contexts or among certain groups. They can add layers of meaning and sentiment to the text. For instance, "It’s a piece of cake" is a colloquialism that means something is easy to deal with or handle. If sentiment analysis models are not trained to recognise these dialect and colloquialism-specific phrases, they may misinterpret the sentiment.
Regarding sarcasm, different types exist. Propositional sarcasm is a non-sentiment proposition but carries an implicit sentiment. For example, someone might say, "Oh, sure, I love spending hours in traffic." The words express a sentiment of paradox, as the speaker is actually expressing frustration or annoyance. Embedded sarcasm can be within words and phrases themselves. For instance, if someone says, "Wow, that's a brilliant idea," with heavy emphasis on "brilliant," it is meant sarcastically to imply the opposite. Like-prefixed sarcasm provides an implied denial of the argument being made. For example, if someone says, Sure, I like spending hours on hold with customer service. It's so much fun!” they are sarcastic, expressing their dislike for the situation. Finally, illocutionary sarcasm can extend beyond speech and involve non-speech acts like body language and gestures. These non-verbal cues contribute to the sarcasm by contradicting the literal meaning of the words. For instance, someone might say, "Oh, great job!" with a sarcastic tone and rolling their eyes. Additionally, when analysing online text, emojis often convey those non-verbal emotional markers, but AI systems may struggle to interpret or understand them. Failure to understand those dimensions and tensions can lead to biases and inaccuracies in sentiment analysis.
Despite these challenges, sentiment analysis using machine learning techniques shows promise. Machine learning models can be trained to detect sarcasm, irony, and negation, which can be particularly helpful for social media sentiment analysis. These models can also learn the affective valence of words, eliminating the need for pre-determined datasets. Furthermore, machine learning-based sentiment analysis methods are faster and often provide more accurate results. However, there are also some limitations to consider. Developers need large or high-quality datasets to achieve accurate sentiment classifications. Noise in the data, such as emojis, slang, or punctuation marks, can reduce accuracy. Additionally, machine learning-based sentiment analysis methods can be more costly than traditional rule-based approaches.
Use-Cases Matter
Now, let's explore some exciting opportunities that sentiment analysis offers.
In the business world, sentiment analysis can help monitor people's attitudes toward your brand, providing an overview of how the public perceives your brand. It can also track changes in these attitudes over time. Additionally, sentiment analysis can be valuable in customer service by evaluating customer satisfaction and the quality of their interactions with your helpdesk.
In healthcare, sentiment analysis can help healthcare service providers collect and evaluate patient moods, track epidemics, identify adverse drug reactions, and gain insights into diseases. For example, sentiment analysis has been used to analyse patient-generated data on social media platforms like Twitter to determine patients' needs and views on healthcare services.
We hope this exploration of sentiment analysis for emotion recognition through natural language processing has sparked your interest. Remember, it's crucial to deploy AI technologies responsibly and ethically.
If you have any questions or thoughts, feel free to reach out!
Until next time,
- Auxane Boch
References and Interesting Reads
Turney, Peter. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 417–424. https://doi.org/10.3115/1073083.1073153
Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pages 79–86. https://doi.org/10.3115/1118693.1118704
Mohammad, Saif M., Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and sentiment in tweets. Special Section of the ACM Transactions on Internet Technology on Argumentation in Social Media, 17(3):1–23. https://doi.org/10.1145/3003433
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780.
Clark EM, James T, Jones CA, Alapati A, Ukandu P, Danforth CM, Dodds PS (2018) A sentiment analysis of breast cancer treatment experiences and healthcare perceptions across twitter. arXiv preprint arXiv:180509959
Saif M. Mohammad; Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis. Computational Linguistics 2022; 48 (2): 239–278. doi: https://doi.org/10.1162/coli_a_00433
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