README.md

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  - Technology Used: Python, TF-IDF word transformation, NLTK library, Scikit-Learn machine learning models, Scikit-Learn's TimeSeriesSplit, Augmented Dickey-Fuller Test, first-differencing, lags
  - Contents: Converting news articles by publishing date into time-series machine elarning forecasting models. Performance comparison between Ridge, Lasso, Random Forest, and XGBoost regression models
  - **Title: "LDA Topic Modeling & VADER Sentiment Analysis for Political News Articles on Events Related to Nigeria in 2019"**
- - [Primary document (Python)](./NLP-Topic Models & Sentiment/Nigeria_News_LDA_&_Sentiment_Analysis.html)
- - [Visualization for the project (R)](./NLP-Topic Models & Sentiment/Nigeria_News_Sentiment_Analysis-Viz-Created-in-R.html)
+ - [Primary document (Python)](./NLP-Topic%20Models%20and%20Sentiment/Nigeria_News_LDA_&_Sentiment_Analysis.html)
+ - [Visualization for the project (R)](./NLP-Topic%20Models%20and%20Sentiment/Nigeria_News_Sentiment_Analysis-Viz-Created-in-R.html)
  - Focus: Topic Modeling & Sentiment Analysis
  - Technology Used: Python, R, Excel, NLTK for stopwords, PorterStemmer, and PunktSentenceTokenizer, gensim library for CoherenceModel, LdaModel, and corpora, Jaccard similarity, vaderSentiment library, itertools, ggplot2
  - Contents: Text data cleaning, Latent Dirichlet Allocation (LDA) topic modeling of Nigerian news article text, VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis scores for articles containing specific political words, compared across quarters of the year.