In recent years, Natural Language Processing (NLP) has made more progress than any other segment of AI. With implications like helping chatbots become more helpful and accurate and allowing companies to gain valuable insights from customer reviews quickly, NLP is assisting companies in finding value within their data with sentiment analysis.

In particular, NLP sentiment analysis can help companies assign a value to text data so that it can be further processed and interpreted by AI and Machine Learning (ML) algorithms. 

Running at rates of more than 90% accuracy, companies can have the confidence that applying sentiment analysis and other NLP techniques to their operations can produce accurate and informative insights, improving efficiency for the business without compromising the customer experience. (Download this white paper, A Benchmark of Popular Sentiment Analysis Models on Financial News, describing an independent study on the accuracy of Accern sentiment analysis.)

What is NLP Sentiment Analysis

Sentiment analysis is a NLP technique that identifies and assesses the emotions or tones detected in-text samples. For example, the process can notice whether the sentiment in a text is positive or negative and to what degree. Whether it be an email, social media post, news story, or report, sentiment analysis can quickly determine the tone and emotions evoked in the text. 

Commonly used across all industries, sentiment analysis is beneficial to test new products, analyze customer reviews, and provide better consumer recommendations. It can also help companies put a quantifiable value to text and enable business leaders to make strategic decisions from that information. Using NLP, sentiment analysis algorithms are built to assist businesses to become more efficient and decrease the level of hands-on labor needed to process text data. 

Types of NLP Sentiment Analysis 

Within sentiment analysis, there are several categories that companies can utilize, with the main four being fine-grained, aspect-based, emotion detection and intent analysis. Each type has its approach and scoring methods, and they can each be used for different purposes and data sets. 

  • Fine-Grained Sentiment Analysis. A fine-grained approach helps determine the polarity of a topic using a scale like positive, neutral, negative, or numerically from negative 10 to 10. This approach helps companies rate reviews and put them on a measurable scale.
  • Aspect-Based Sentiment Analysis. For even more precision, an aspect-based process determines what item is being rated and can evaluate which sentiment is applied to which aspect from a string of text.
  • Emotion Detection Sentiment Analysis. Emotion detection pinpoints a specific emotion being expressed, such as anxiety, excitement, fear, worry, or happiness, while intent analysis helps determine the intent behind the text.
  • Intent Sentiment Analysis. Intent analysis can be applied to reviews, comments, social media posts, feedback, etc and can provide deep insights into sentiment.  

Given the nuances of human language and emotions, sentiment analysis is most effective for companies when they choose the proper analysis algorithms that match the data set they are trying to analyze and how they will use the insights.

Three Reasons to Use NLP Sentiment Analysis in Financial Services

Traditionally, analyzing text data requires significant time and manual labor to sift through large amounts of data and comb through the latest news stories, earnings calls, quarterly filings, etc. However, sentiment analysis allows financial professionals to focus on value-add tasks and spend less time determining the importance of each new development within the industry. 

By helping companies cut out the noise of the news cycle and extract the most valuable insights to inform their investment decisions, sentiment analysis can be a valuable tool to all financial professionals.

Obtain Relevant Information

With all the data available to financial professionals across various platforms, sentiment analysis can help sort through large amounts of text and information and provide an accurate assessment of the possible implications and tone. It would be impossible for one individual to sort through the same volume of data and determine what’s relevant and valuable in today’s information age.

Financial services firms can utilize sentiment analysis to nail down only the most crucial and consequential data based on the parameters set for the algorithm. It can also keep investors and portfolio managers from being bogged down by the constant flow of news and reporting.

For example, a portfolio manager may want to take a short position on a specific stock and is only interested in news stories related to that company with negative implications. Therefore, sentiment analysis could help filter only articles or news stories with a negative skew rather than showing each new filing or immaterial development related to the company.

Analyze Market Sentiment and Financial News

Each day there are mountains of news stories to sift through, primarily related to the financial markets around the globe. Between earnings reports and new data releases from the Fed, it can become difficult for financial professionals to stay well-informed without letting a vital news item slip through the cracks. 

To stay on top of trends and remain well-informed without spending hours each day reading dense reports and articles, finance professionals can tap into the benefits of AI and NLP.

Sentiment analysis is helpful for financial professionals to stay up-to-date on the overall markets and get a quick and clear overview of how other prominent professionals in the field feel about recent trends or developments.

Overall, sentiment analysis can lead to quicker trade decisions, faster due diligence, and a more comprehensive view of the markets.  

Assist in the Investment Decision-Making Process

Sentiment analysis is now helping investors and portfolio managers do something that’s always been a dream for the industry– to predict future stock movements. Based on developments in the news, recent reports, and more, sentiment analysis can help find potential trade opportunities and forecast upcoming swings in a stock price. 

Beating competitors and outperforming peers is a crucial part of being successful as an investor, and sentiment analysis can give financial professionals a competitive advantage by providing rapid and accurate insights into the industry that they can act on. 

Forecasting future stock moves is crucial for investors to remain competitive and deliver positive results for their clients. So, by using AI to extract positive or negative sentiments on a specific company or industry from news developments, portfolio managers and investors can easily make informed investment decisions before competitors.  

Using No-Code NLP for Sentiment Analysis

When done correctly, sentiment analysis can be groundbreaking for financial professionals; and with a fast and automated solution, such as the Accern NoCodeNLP Platform, Citizen Data Scientists, such as Research and Business Analysts have sentiment analysis at their fingertips, without technological complexities. They don’t need to learn how to code or depend on scare resources, such as data specialists and software engineers.

To be at the cutting-edge of technology and implement AI/ML/NLP algorithms into their operations, companies can gain an edge over their competitors and easily scout out new opportunities.

Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can drive sentiment analysis for business ROI.

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