Sentiment analysis using nlp
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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.
Sentiment analysis using nlp
- 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.