This class provides several methods for utilizing LDA Analysis for topic identification. The purpose of these functions is to allow a user to input a CSV file containing users and tweets from Twitter. The functions provided here are then used to parse the tweets, stem and remove stop words, and then estimate an LDA model based of the data.

Once the model has been created, an inference can be performed on the CSV data. When inference has been performed, the user can then run the evaluation step. This step analyzes the raw data and calculates the weight of identified topics for individual users.

Finally, once the weight of each topic per user has been calculated, the labeling function can provide a report in CSV format w/ vectors for the selected topics and labels. The final function SHOULD only be run once the generated tword file (From inferencing step) has been read through, and topics of interest have been identified and labeled.