The document discusses using sentiment analysis with natural language processing (NLP) to analyze investor sentiments and improve stock market decision making. It describes a 3 phase NLP model built with Python's NLTK toolkit: 1) pre-processing data, 2) scraping online sources to determine sentiment towards a stock, 3) using machine learning classifiers and sentiment scores to optimize results. The model aims to help executives decide whether to buy, hold, or sell a company's stock through more accurate sentiment forecasting.

1. Sentiment Analysis using NLP

2. Website: www.anikatechnologies.com Email: sales@anikatechnologies.com Phone: 91 7719882295
Purpose
The variability of stock prices makes it difficult for investors to spot market trends. One
of the techniques that can result in improved forecasting of trends is sentiment analysis
using natural language processing (NLP).
In this whitepaper, you will learn how sentiment analysis using NLP can help an investor
in improved decision making.
Background
Sentiment analysis using NLP involves categorising opinions gathered from different
sources to determine the attitude of a group of individuals towards a subject. The
technique aims to create an increased awareness of positive, negative, or neutral
sentiments regarding a subject.
Using the technique allows processing of millions of user sentiments in seconds rather
than hours it would take a team to complete manually.
Our NLP Model – Enhanced Sentiment Analysis Using Python NLTK
We have built an algorithm/model to analyse the sentiments towards a particular
company in the stock market using Python's Natural Learning Toolkit (NLTK).
The analysis is carried out in three phases as described below.
Phase I
An essential step in NLTK is the pre-processing of data before actual analysis. The Python
toolkit works on a consistent set of data based on specific algorithmic directives. You can
think of the step as a data cleaning procedure that is performed before the actual
analysis of data. This helps to eliminate irrelevant information and also speeds up the
analysis.

3. Website: www.anikatechnologies.com Email: sales@anikatechnologies.com Phone: 91 7719882295
Phase II
After pre-processing of data, our NLP model tries to get all the relevant information
about a particular company or stock by scraping information from online sources such
as,
 News articles,
 Tweets,
 Message boards,
 Business reports and
 Stock indices.
Next, our algorithm determines the sentiment associated with the stock.
Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analyser that is
included in Python's NLTK package is used to assess whether the sentiment is positive,
negative, or neutral.
Phase III
Using Python NLTK's scikit-learn library, different machine learning models can be
created such as multi-layer perception (MLP) Classifiers and Random Forest. The
sentiment score can be fed into these models for optimised results regarding investor
sentiments.
Summary
Sentiment analysis using NLP/NLTK technique can help executives to decide whether to
buy, hold, or sell a particular company's stock. Using our sentiment analysis model can
lead to a more accurate forecast, as we have the backing of technical analysis model.
Apart from improved stock market decision, our NLP/NLTK model can be used for
reputation management. It can help executives to analyse social media mentions and
other online information to know about customer's view regarding a product, service,
brand, or a marketing campaign.