GitHub / AYSE-DUMAN / Sentiment-Analysis-on-Stocks-Data-using-NLP

This study is about creating a sensitivity classifier model using messages from customers. We have a binary classification problem that categorizes stock sensitivity data as positive or negative. 1 indicates positive sentiment and 0 indicates negative sentiment. The main resource I used in the study is the Python & Machine Learning for Financial Analysis course on Udemy. The main steps are as follows: Importing required libraries(pandas,numpy,seaborn,matplotlib,nltk,gensim,tensorflow) Explanatory Data Analysis Data cleaning (removing punctuations and stopwords from text) Visualization of cleaned dataset and plotting wordcloud Prepare the data by tokenizing and padding Building a custom-based deep neural network for sentiment analysis (embedding layer, LSTM network) Making prediction and assessing the model performance (confusion matrix)

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AYSE-DUMAN%2FSentiment-Analysis-on-Stocks-Data-using-NLP

Topics: keras-tensorflow, lstm-neural-networks, natural-language-processing, nltk, numpy, padding, pandas, sentiment-analysis, wordcloud

• Files
• Readme
• Dependencies