A Step-by-Step Tutorial for Conducting Sentiment Analysis. In this article, I will discuss the use of logistic regression, and some interest results I found from my project.
Following the steps from my previous articles, I preprocessed the text data and transformed the “cleaned” data into a sparse matrix. Please follow the links to check out more details.
Now I am at the last step of conducting news sentiment analysis on WTI crude oil future prices. In this article, I will discuss the use of logistic regression, and some interest results I found from my project. I have some background introduction to this project here.
Define and Construct the Target Value
As discussed briefly in my previous articles, conducting sentiment analysis is solving a classification problem (usually binary) by machine learning models and text data. Solving a classification problem is solving a supervised machine learning problem, which requires both features and target values when training the model. If it is a binary classification problem, the target values are usually positive sentiment and negative sentiment. They are assigned and detailedly defined depending on the context of your research question.
Take my project as an example, the purpose of my project is to predict the change of the crude oil future prices from recently released news articles. I define the positive news as the ones that would predict the price increase, while the negative ones would predict the price decrease. Since I have already collected and transformed the text data and will use them as the features, I now need to assign the target values for my dataset.
The target value of my project is the directions of price change with respect to different news articles. I collected the high-frequency trading data from Bloomberg for the WTI crude oil future close price, which is updating every five minutes.
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.
Sentimental Analysis Using SVM(Support Vector Machine). Sentimental analysis is the process of classifying various posts and comments of any social media into negative or positive.
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
PySpark in Machine Learning | Data Science | Machine Learning | Python. PySpark is the API of Python to support the framework of Apache Spark. Apache Spark is the component of Hadoop Ecosystem, which is now getting very popular with the big data frameworks.
You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.