Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes and categories within a sentence or a phrase.
In today's communications and news consumption, the headlines of various articles play an even more important role than before. Now, we use sentiment analysis on the headlines of a particular company or companies to determine the effects of the headlines on the stock market. In this project, I used the stock news headline dataset to calculate the sentiment against binary class.
(https://www.kaggle.com/geminikeggler/stock-sentiment-analysis)
- Load the data
- Preprocess text data
- Build and trainthe Model
- Evaluate the model against the test set
- Bage of Words
- TFIDF
- Word2Vec
- Random Forest
- LSTM
- Ensemble Models
- Decision Tree
All the code and comments are listed in the jupyter notbook (Stock Sentiment Analysis using News Headlines .ipynb)