OpenAI vs Watson NLP: Emotion and Tone Analysis
Analyzing the Strengths and Weaknesses of OpenAI and Watson NLP in Emotion and Tone Analysis
In the last blog on OpenAI vs Watson NLP: Sentiment Analysis, we have seen the comparison between OpenAI and Watson NLP for Sentiment analysis. Now, we will compare the two for the tasks for Emotion and Tone Analysis.
Emotion and Tone analysis is a process of evaluating the emotional tone or attitude conveyed by a piece of text. It involves analyzing the language used and identifying the emotions and attitudes that are conveyed by the words and phrases.
Emotion and Tone analysis typically involves a more nuanced evaluation of the emotional content of text compared to sentiment analysis, which often focuses on identifying whether the overall sentiment of the text is positive, negative, or neutral. In tone analysis, the emphasis is on identifying the specific emotions and attitudes that are expressed, such as anger, happiness, sadness, sarcasm, or irony.
Emotion and Tone analysis can be useful in a variety of contexts, such as understanding the tone of customer feedback, evaluating the tone of political speeches or media coverage, or assessing the tone of written communication in a workplace or academic setting. It can also be used in combination with sentiment analysis to provide a more comprehensive understanding of the emotional content of a piece of text.
Tone Analysis
The tone analysis in OpenAI provides only two tones positive and negative which is no different from sentiment classification.
The Watson NLP on the other hand provides granular level tones; Excited, satisfied, sad, frustrated, polite, impolite, and sympathetic.
Therefore, Watson NLP is a clear winner for the Tone Analysis task as the other factors around customizability, rate, and token limits are the same as the other NLP tasks described in the previous blog: OpenAI vs Watson NLP: Sentiment Analysis.
Emotion Classification
Emotion classification can also be done out of the box using a pre-trained model both in the OpenAI and the Watson NLP library.
OpenAI output
Emotion: Joy (confidence score: 0.9)Watson NLP output:
The only difference in emotion classification is again the customizability, rate, and token limits in which the Watson NLP is the winner.
Conclusion
We have seen how the NLP tasks Emotion and Tone analysis can be carried out both using OpenAI and the Watson NLP. However, in terms of customizability, rate limit, and token limit, Watson NLP is the clear winner for both Emotion and Tony analysis tasks.
If you want to read about our comparative analysis for the tasks like Entity, Keywords, and PII extraction, you can read this blog.
