sentiment-analysis

Plan of Action

Load Training.1600000.processed.noemoticon.csv Dataset (1.6 Mn twitter tweets) and a Twitter Finance Tweets Datasets
Pre-process dataset by removing special characters, numbers, etc. from user reviews + convert sentiment labels positive & negative to numbers 1 & 0, respectively
Import GloVe Word Embedding to build Embedding Dictionary + Use this to build Embedding Matrix for our Corpus
Model Training using Deep Learning in Keras for separate: Simple Neural Net, CNN, LSTM and DistilBERT Models and analyse model performance and results
Last, perform predictions on real L&T tweets

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
src		src
README.md		README.md
Scraping.ipynb		Scraping.ipynb
Sentiment.ipynb		Sentiment.ipynb
TweetExtractionUsingTweepy.ipynb		TweetExtractionUsingTweepy.ipynb
b2_preprocessing_function.py		b2_preprocessing_function.py
model-final-Copy1(1).ipynb		model-final-Copy1(1).ipynb
model-final.ipynb		model-final.ipynb
pip_requirements.txt		pip_requirements.txt
predictions.csv		predictions.csv
sent_train.csv		sent_train.csv
sentiment-prediction.ipynb		sentiment-prediction.ipynb
test-prediction.ipynb		test-prediction.ipynb
test.csv		test.csv
twitter_LT.csv		twitter_LT.csv

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