NLP From A Time Series Perspective. How time series analysis can complement NLP. Text Summarization (i.e. summarize a text in order to gain a better understanding of it) Text Classification (e.g. classifying text based on certain features such as detecting spam emails)
On the face of it, Natural Language Processing (NLP) and time series analysis do not necessarily appear to have that much in common.
In the context of data science, the main reasons for analysing text are typically as follows:
I particularly wish to address the domains of text classification and sentiment analysis in this regard.
Let’s consider an example. Suppose that one built a sentiment analysis model in 2019 in order to gauge sentiment on travel. Data might have been collated from a variety of social networks, e.g. Twitter, Reddit, etc.
Chances are — sentiment on travel might have still been quite positive — notwithstanding a degree of concern due to the impacts of travel on climate change.
However, 2020 is a vastly different landscape for travel (or lack thereof), with air passenger numbers having plummeted as a result of the COVID-19 pandemic.
As a result, any sentiment model that would have been trained on 2019 data would likely perform quite poorly if run today. Travel restrictions, virus fears, and economic concerns are likely to have been under-represented in any corpus that would have been used to train a text classification model to gauge travel sentiment. Moreover, the term “COVID-19” did not exist before this year, and a text classification model would not know to assign a negative sentiment to this term in the context of travel.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also