Stock Sentiment Analysis Using Autoencoders

Stock Sentiment Analysis Using Autoencoders

In this notebook, we will use autoencoders to do stock sentiment analysis. Autoencoder consists of encoder and decoder models. Encoders compress the data and decoders decompress it. Once you train an autoencoder neural network, the encoder can be used to train a different machine learning model.

For stock sentiment analysis, we will first use encoder for the feature extraction and then use these features to train a machine learning model to classify the stock tweets. To learn more about Autoencoders check out the following link...

https://www.nbshare.io/notebook/86916405/Understanding-Autoencoders-With-Examples/

Stock Tweets Data

Let us import the necessary packages.

In [1]:

# importing necessary lib 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

In [2]:

# reading tweets data
df=pd.read_csv('/content/stocktwits (2).csv')

In [3]:

df.head()

Out[3]:

	ticker	message

(continued...)

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