Sentiments Analysis of Financial News as an Indicator for Amazon Stock Price

Sentiments Analysis of Financial News as an Indicator for Amazon Stock Price

From fundamental ratios, technical indicators to news headlines and insider ... Extract stock sentiments from financial news headlines in FinViz website using Python ... An example of the news headlines section for Amazon (with ticker 'AMZN') ... to add the stock ticker at the end of this url '' to ...

Sentiments analysis of news has become one of the most robust ways of generating buy/sell signals for stocks in all major developed and major emerging markets. The idea is simple, a cumulative sentiments score of the news articles mentioning a companies name, brand, stock ticker, etc. will serve as a great indicator for the next days’ closing stock price.

This only works with stocks that have high trading volumes and active news coverage across major outlets. Generally speaking, constituent stocks of major market indices such as NASDAQ, Dow Jones, or S&P 500 will all satisfy these criteria.

In this article, we will discuss the steps necessary for building such a sentiments analysis pipeline for stock.

You will have to select which portions of the page you want to extract. Typically, people want to extract author names, dates, titles, and full text of the news article.

Plotting Amazon Stock Price

Before we get into actually getting sentiments data, let us first get stock market data for Amazon stock price (AMZN). I like to use Alphavantage’s API to get stock market data; it’s free to use but you will have to generate an API key.

import requests 
import json
from dateutil import parser

import requests
import json
test_url = '' + API_KEY + '&datatype=csv'
r = requests.get(url = test_url)
print("Status Code: ", r.status_code)
html_response = r.text
with open("amazon_stock.csv", "w") as outfile: 
from dateutil import parser
datetime_obj = lambda x: parser.parse(x)
df2 = pd.read_csv("amazon_stock.csv", parse_dates=['timestamp'], date_parser=datetime_obj)
#df2 = df2[(df2["timestamp"] >= start_date) & (df2["timestamp"] <= end_date)]
## Output
timestamp   open    high    low     close   adjusted_close  volume  dividend_amount     split_coefficient
0   2020-09-28  3148.85     3175.04     3117.1684   3174.05     3174.05     4224165     0.0     1.0

Next, we will plot this so that we can visually see the price movements. As you might already know, the stock has had a strong rally due to covid-19 pandemic.

import matplotlib.pyplot as plt 
import seaborn as sns 
top = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4) top.plot(df2['timestamp'], df2['close'], label = 'Closing price') plt.title('Amazon Close Price') 
plt.legend(loc=2) bottom = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4)["timestamp"], df2["volume"]) 
plt.title('Amazon Daily Trading Volume') 

data-science naturallanguageprocessing nlp web-scraping artificial-intelligence

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Web-Scraping and Pre-Processing for NLP

Using Python to scrape and process text data from the web. For Natural Language Processing, clean data is important. Even more so when that data is coming from the web.

AI or Data Science? | Artificial Intelligence And Data Science Career

There are many intersections and overlaps between AI and data science. AI has numerous subsets, like Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). With many career opportunities in both fields, there are lots of conflicting perspectives on educational paths for starting a career in one of these fields.

Applications Of Data Science On 3D Imagery Data

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

Data Science Course in Dallas

Become a data analysis expert using the R programming language in this [data science]( "data science") certification training in Dallas, TX. You will master data...