How Stock Sentiment Analysis And Summarization Is Conducted Using Web Scraping?

By Author: Stock Management
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It is due to the fact that individuals have far less time, but this is where AI can help. Automatic summarization and online scraping appear to help us obtain the knowledge we need to make the best decisions.

Module References
1. Web Scraping Modules

Requests Module
For web scrapers, the request module is a blessing. It enables developers to retrieve the target webpage's HTML code.

BeautifulSoup
Unless you're a web developer, BeautifulSoup will come in handy because it breaks down a complex HTML page into a legible and scrapable soup object.

2. Standard Modules

Pandas Module
It's a well-known technique in a data developer's toolbox for dealing with enormous amounts of data and gaining inference or seeking information through direct correlation, combining, filtering, and expanded data analysis.

Numpy Module
To put it another way, it makes doing mathematical operations on the data. The heart of this module is the use of matrices and array calculations. Pandas is also based on it.

Matplotlib
Consumers, of obviously, like to see cool images, and ...
... visuals communicate a fair bit better than text on a screen. Matplotlib will take care of the rest.

3. Sentiment Analyzer Module

NLTK
It works by analyzing text data and inferring feelings from it. When it comes to Natural Language Processing, Hugging Face Robots and NLTK have a competitive advantage in the current market.

Textblob
During the first phase of my project, you can employ a light-weight sentiment analyzer.

Transformers Pipeline Sentiment
Transformer's arsenal includes a sentiment analyzer.

4. Article Summarization

Newspaper3K
It's a simple abstractive summary python module that assists you in summarizing a text.

Transformers(Financial-Summarization-Pegasus)
A deep learning toolkit primarily for NLP projects. Pegasus financial summary will be used in this project.

1. Install and Import Dependencies

Install pip... Essentially, we're just using run command in the background to download the latest the appropriate packages in our system so that we can access them in our code.

For the sake of convenience, pip will install all of the required packages for this project.

2. Summarization Modules

The summarizing models reduce the provided material to a logical and succinct summary.

Example: Financial-summarization-pegasus (Huggingface): It is pre-trained on financial language in order to extract the best summary from financial data.

Input:

In the largest financial buyout this year, National Commercial Bank (NCB), Saudi Arabia's top lender by assets, agreed to buy rival Samba Financial Group for $15 billion. According to a statement issued on Sunday, NCB will pay 28.45 riyals (US$7.58) each Samba share, valuing the company at 55.7 billion riyals. NCB will issue 0.739 new shares for every Samba share, which is at the lower end of the 0.736–0.787 ratio agreed upon by the banks when they signed an initial framework deal in June. The offer represents a 3.5 percent premium over Samba's closing price of 27.50 riyals on Oct. 8 and a 24 percent premium over the level at which the shares traded before the talks were made public. The merger talks were initially reported by Bloomberg News. The new bank will have total assets of more than 220 billion dollars, making it the third-largest lender in the Gulf area. The entity's market capitalization of 46 billion dollars is almost identical to Qatar National Bank's.

Output:

The NCB will pay 28.45 riyals per Samba share. The deal will create the third-largest lender in the Gulf area.

3. A News and Sentiment Pipeline: Finiviz website

Finiviz is the website that is being considered in this pipeline. It's a web-based application that lists securities and the most recent stock stories in chronological order. The goal of this pipeline is to extract the URLs, as well as their headlines and dates, and do sentiment analysis on the headlines.

User Defined Functions used in Pipeline 1:
1. Function: Finiviz_parser_data(ticker):

Using the requests library, this method collects data from the Finviz website. The downloaded item should thereafter have a response code of at least 200.

The HTML response is parsed and returned as soup using the Beautiful Soup class. It should be mentioned that soup is a bs4 food. BeautifulSoup.