How is Vectorization used in Stock Sentiment Analysis?

Vectorization is an important early stage of the stock sentiment analysis process. Here’s how the process is used to help traders gain a competitive advantage.

What is NLP vectorization?

Computers do not understand words, but they can understand numbers. NLP Vectorization is the process that converts text into structured numerical data that computers can interpret. 

Human language is rich in nuance, with some words and phrases having multiple meanings depending on the context, tone and punctuation. Think of the famous phrase “Eats shoots and leaves”. By placing a comma at various places throughout the phrase, the meaning changes entirely, leading to this joke from Ursula Le Guin:

“A panda walks into a café. He orders a sandwich, eats it, then draws a gun and fires two shots in the air.

“Why?” asks the confused waiter, as the panda makes towards the exit. The panda produces a badly punctuated wildlife manual and tosses it over his shoulder.

“I’m a panda,” he says at the door. “Look it up.”

The waiter turns to the relevant entry in the manual and, sure enough, finds an explanation.

“Panda. Large black-and-white bear-like mammal, native to China. Eats, shoots and leaves.”

Whilst not many of us talk about the activities of pandas on a daily basis, the punctuation and spelling we use on a daily basis is often far from perfect. 

For computers to be able to understand and analyse human language, they need highly sophisticated solutions. 

Vectorization converts words and phrases into numerical sequences, which are known as vectors. These vectors can be processed by machine learning algorithms to “understand” the input phrases. 

The evolution of keyword matching

Early language systems relied upon keyword matching. Whilst very advanced for the time, keyword matching often struggled to understand nuance. Take these two sentences:

• “The company missed earnings expectations but has promising growth projections”
• “The company beat earnings expectations but warned of slowing sales next quarter”

Both statements include a mixture of positive and negative phrases, but understanding the sentiment behind both is more nuanced. 

If a stock sentiment system relied upon keyword matching, it would likely struggle to correctly classify the phrases as positive or negative. 

Vectorization however, allows computers to capture the nuance by considering the context, word relationships and overall tone of the statements.

Types of NLP vectorization

In the early days of vectorization, the ‘Bag of Words’ approach was common. This technique focused upon word frequency, ignoring other factors such as grammar and word order. 

Whilst this approach was straightforward and easy to implement, it was limited in its sentiment analysis accuracy. 

For example, ‘not good’ would be evaluated the same as ‘good’.

Term frequency-inverse document frequency was an advancement upon the bag of words approach by introducing a weighting system to the frequency of words appearing within a passage. 

It is commonly used in information retrieval and for text analysis based machine learning, but it still offers very little in the way of understanding the deep semantic meaning of words. 

Word embedding

Word embedding systems represented a significant breakthrough in the accuracy of sentiment analysis systems. 

This approach laid the foundation for computers to understand the relationships between words, rather than treating the words as single entities in one dimension. 

There are two significant word embedding methods, one being Word2Vec, which predicts the surrounding words of any given target word, as well as the GloVe approach that leverages global statistics to create word embedding predictions. 

Contextual Embeddings

It is the contextual embedding models such as BERT and RoBERTa that have powered the meteoric rise of stock sentiment analysis tools in recent years. These systems generate different vectors for the same word, depending on the surrounding context of words around it. For example:

• In the phrase “he made a bank deposit”, the word “bank” refers to a financial institution
• In the phrase “he sat by the bank of a river”, the word “bank” refers to a riverbank

These Bidirectional Encoder Representations from Transformers understand the difference between the various meanings of individual words, and can assign separate vectors accordingly. 

This helps contextual embedding vectorization systems to capture a deep, nuanced understanding of human language – however the computational power required is quite resource intensive!

Why does accurate vectorization matter when analyzing stocks?

Quantitative stock analysis relies upon timely and accurate interpretation of data. Time is of the essence when it comes to day trading. 

Price charts and earnings reports offer valuable quantitative insights, but the best traders need to understand the narrative behind the numbers; how the markets feel about a stock, not just what the numbers are saying. That’s where vectorization comes into play.

Financial sentiment lives within unstructured texts, such as headlines, analyst reports and social media posts. 

These vital bits of financial information are constantly evolving, full of nuance and not neatly structured like a spreadsheet. 

Vectorization enables financial textual data to be analyzed by stock sentiment analysis models, creating data that can be measured, compared and acted upon. 

This sensitivity to context is crucial in stock analysis, where sentiment can be a leading indicator of price movements. Sudden shifts in sentiment vectors can be interpreted as an early warning signal. 

This sort of stock sentiment analysis is becoming increasingly commonplace within quantitative hedge funds, where vectorized stock sentiment data is used as a source of alpha. 

Using advanced vectorization for your stock sentiment analysis

StockGeist.ai is the platform to actively monitor the current popularity of over 2200 publicly traded companies. We use advanced vectorization techniques to provide accurate, real-time stock sentiment data. 

You can even integrate this stock market sentiment data into your project using our API. 
Sign up at the StockGeist API dashboard, get your API key and enjoy 10k free credits!