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, AI is assisting companies in finding value within their data. A component of NLP, sentiment analysis, can help companies easily assign a value to text data to be processed and interpreted by AI and machine learning algorithms.
Running at rates of 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.
What is Sentiment Analysis?
Sentiment analysis is a natural language processing 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 AI, 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.
Given the complex nature of emotions paired with this advanced technology, there is no doubt that some questions remain about its potential uses. While there is still some concern over the accuracy and application of sentiment analysis, it isn't going away any time soon. It will continue to become more accurate and robust the more it is utilized and tested by professionals and companies in all industries.
Types of 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: 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: 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: 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: Intent analysis is the process of determining the intention behind any text, content, or data. 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 approach that matches the data set they are trying to analyze and how they will use the insights.
Reasons to Use Sentiment Analysis within Financial Services
Among the many ways AI is reshaping the financial industry, sentiment analysis is a beneficial way firms can utilize AI. 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.
Traditionally, analyzing 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.
1. To 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.
2. To Analyze Market Sentiment & 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.
3. To 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 AI for Sentiment Analysis
Financial services firms can quickly implement sentiment analysis into their business by utilizing a no-code AI solution. With this in their toolkit, firms don't have to hire data specialists or software engineers to build the system from scratch or retrain current employees to learn how to code—instead, they can extract insights from sentiment analysis quickly and with ease. When done correctly, sentiment analysis can be groundbreaking for financial professionals. Firms can use a no-code AI solution to ensure that the system is set up correctly without guesswork.
To be at the cutting-edge of technology and implement AI algorithms into their operations, firms can gain an edge over their competitors and easily scout out new opportunities others can't. With a no-code AI solution, all financial professionals can have sentiment analysis at their fingertips without technological complexities. To see how a no-code AI solution could help your firm with sentiment analysis, request a free demo with Accern today.
