Temporal is used as a term to describe the process of analyzing text data over time. This can be used to track changes in sentiment, topic, or other aspects of the text data. Temporal analysis can be used to identify trends, patterns, and outliers in the data.
Some applications of temporal analysis include:
- tracking changes in sentiment over time
- tracking changes in topic over time
- tracking changes in the relationships between entities in a text over time
- identifying trends, patterns, and outliers in the data.
Problem Formulation
Problem formulation of temporal analysis can be defined in many ways, depending on the application. For example, one could formulate the problem as a classification problem, where the goal is to predict whether a text document is about a certain topic at a certain time point. Another formulation could focus on identifying changes in sentiment over time for a given topic.
There are many different methods that can be used for temporal analysis. Some common methods include:
- Time series analysis: This method looks at the data over time and identifies trends, patterns, and outliers.
- Sequential pattern mining: This method looks for sequences of events in the data and identifies patterns.
- Topic modeling: This method can be used to identify changes in the topics that are being discussed over time.
- Sentiment analysis: This method can be used to track changes in sentiment over time.
Data Preparation
Data preparation for temporal analysis generally requires pre-processing of the text data. This can include tasks such as tokenization, lemmatization, and stopword removal. It is also common to create a time series for each text document. This can be done by splitting the text document into a series of smaller documents, each containing a certain number of words. The time series can then be created by taking the mean or median of each document.
Evaluation of temporal analysis generally requires a clear understanding of the application and the desired outcome. Evaluation metrics will vary depending on the application. For example, if the goal is to track changes in sentiment over time, then a common metric would be accuracy. If the goal is to identify patterns in the data, then a common metric would be precision.