Sentiment analysis on tweets to understand what people in India think about China. By analyzing the sentiment (positive/negative/neutral) expressed in these tweets, we could gauge public opinion and identify recurring themes or issues related to China. Impact: Understanding Public Opinion: Provided a clear picture of the general sentiment towards China in India, highlighting periods of heightened positivity or negativity. Media and Communication Strategies: Assisted media outlets and communication teams in tailoring their content and messaging based on current public sentiment trends.
One such very creative use of Natural Language Processing that I have used in my work has been in chatbot development. This way, using NLP, the chatbot better understands and responds to user queries in a way that is more like a human and interactive. For example, instead of recognizing some keywords, a chatbot can grasp the context and intent behind the user's question. It can, therefore, deal with more complex and varied inquiries, providing correct and helpful answers. The impact was huge because users found the chatbot to be very engaging and effective, resulting in increased satisfaction and more meaningful engagements. This has freed my team from time-wasting, as the chatbot is able to answer routine questions and perform routine tasks, freeing human time to deal with complex issues.
In our work, we've used the NLP models to facilitate and speed-up the annotation process. A number of our clients also use the existing models to cut down the time for the humans-in-the-loop training. For simple text labeling tasks like NER, anonymization, sentiment analysis and summarization, a mix of humans and pre-annotations generated by LLMs prove to be most time-efficient and cost-effective. However, it's too early to rely completely on the annotations provided by NLP models. At least a small portion of the data must be randomly sampled and checked by the human QA/annotation specialist.
There are 2 times when I used NLP in innovative way. The first one, I had a dataset that had only categorical features, so decided, instead of trying to decide what type of pre-processing I should do, I just concatenated them into a list of words, and used NLP pre-processing approaches. The second one, I had numerical features, over 28,000 of them, but no traditional data science approaches worked, I modified the problem so instead of having numerical features, I replaced the numerical features by their name and concatenated the names and then used NLP pre-processing.
We saw huge benefits from using Natural Language Processing (NLP) to pair content with appropriate campaigns automatically. This approach improved our performance significantly by analyzing the content and identifying the best matching campaigns. We utilized NLP techniques like sentiment analysis, keyword extraction, and topic modeling to achieve this. The results have been outstanding, boosting both marketing effectiveness and operational efficiency.