Sentiment Analysis Stock Market: Sources and Challenges

Making accurate predictions regarding stock prices is challenging as the stock prices move depending on factors like interest rates, corporate governance, investors’ risk aversion, market trends, and firm investments. However, understanding the market psychology with sentiment analysis stock market might give a clue about future stock price movements.

Explore the definition, ways, challenges, and channels of stock market sentiment analysis, as well as a special sentiment analysis model, BERT:

What is stock market sentiment analysis?

Figure 1. Time series plot of news sentiment score vs. actual stock prices

Source: Arxiv¹

Not only do financial determinants such as investments, profitability, and corporate governance quality influence stock prices, but also public perception of companies, brand reputation among customers, and other non-financial factors play significant roles.² Stock market sentiment analysis is one of the web scraping methods for finance that helps to understand public views on firms to make informed business decisions.

Research shows that financial market price movements correlate with public sentiments regarding the companies.³  Moreover, when users’ sentiments are considered in making price forecasts, the accuracy of the prediction models increases by 20%, showing the additional value of customer sentiment in predicting prices.

Thus, sentiment about the company in the media, industry reports, financial reports, social media reviews, or investors’ opinions can provide great insights into the stock price movements.

Learn more about the use cases of web scraping for finance besides sentiment analysis.

How sentiment analysis stock market works

Figure 2. Diagram of stock market sentiment analysis flows

Source: Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning⁴

Sentiment analysis leverages NLP and machine learning algorithms to analyze market data from diverse sources like news articles, social media, financial reports, and customer feedback. This data undergoes preprocessing, including tokenization and noise removal, to extract meaningful insights.

Data labeling has an essential role in sentiment analysis based on categorizing emotional expressions as either negative, positive, or neutral. Labeling data creates a functional, reliable model because the algorithm’s texts, images, or speeches are tagged with meaningful labels and classified into different groups. It is one of the building blocks of machine learning models, and the models learn these labels that allow for making further classifications.

Various algorithms, such as rule-based methods, lexicon-based methods, and advanced machine learning models, classify sentiment as positive, negative, or neutral. These classifications provide traders with valuable insights into market sentiment trends and potential movements.

Where to find data for sentiment analysis in stock market predictions

1. RSS feeds

An online file known as an RSS news feed allows users to access website material in a standardized format. RSS feeds provide a continuous stream of updates from financial news websites and blogs. They are invaluable for tracking market developments in real time. By subscribing to relevant financial news RSS feeds, traders can gather a wealth of information on financial markets, market trends, company announcements, and economic indicators.

These news might include technical analysis of market movements done by analysts. In some example, technical analysts might advice to buy , sell, or hold the analyzed stocks in order to make profit. NLP-based news feeds help understand the sentiment toward a company and provide insights into the economic conditions, investment opportunities, systematic and unsystematic risk, market volatility, and potential shifts in the market.

2. Company websites

The websites of companies are a vast repository of information useful for sentiment analysis. A recent study analyzed sentiment toward 87 companies on the websites for seven years.⁵ Researchers found a statistically significant relationship between text sentiment and stock price movements. Results indicate that changes in sentiment are the most powerful indicator of market performance. Results show that the market performance in the finance sector is affected the most by the sentiment change.

3. Social Media 

Social media platforms like Twitter, Reddit, and LinkedIn have become significant sources of real-time sentiment data. Investors and analysts frequently share their opinions and insights on these platforms, making them rich sources of sentiment indicators. Natural language processing (NLP) techniques can be applied to extract sentiment from social media posts.

The positive association between investors’ sentiment and the market prices is proven.⁶ Twitter is a great alternative data source for analyzing public opinion. A recent study shows that the accuracy of sentiment analysis of Twitter posts is almost 90%.⁷

Figure 3. Cathie Wood shares her opinion on Twitter about the company sales

Investors express their opinions on purchases, costs, the stock market, or financial solutions. The market prices and investor sentiment are related so that when these views are positive, the market’s stock prices tend to rise. Thus, analyzing investor sentiment using sentiment analysis techniques might offer important clues about the stock market’s future.

4. Financial reports

Quarterly and annual financial reports published by companies provide essential data for sentiment analysis. These reports include information on earnings, revenue, and other financial metrics, which can influence investor sentiment. Analyzing the language and tone of these reports can also yield insights into the company’s outlook.

5. Economic indicators and reports

Government and international organizations publish economic indicators and reports that are crucial for sentiment analysis. These indicators include data on employment rates, inflation, GDP growth, and more, which influence overall market sentiment.

You can also check out our data-driven list of sentiment analysis services.

Challenges in stock market sentiment analysis

Stock market sentiment analysis faces several challenges that can impact its accuracy and reliability.

1. Data accuracy and noise filtering

Social media and news articles often contain a mix of relevant and irrelevant information, requiring sophisticated algorithms to differentiate between the two.

2. Dynamic nature of language

The same phrase can have different meanings depending on context, making it difficult for natural language processing (NLP) tools to consistently interpret sentiment correctly.

3. Market psychology and human behavior

These determinants are complex and influenced by numerous unpredictable factors, such as geopolitical events and sudden economic changes, which can abruptly shift sentiment and market trends.

4. Accurate sentiment classification model

Since market conditions and human behavior are complex phenomena, sentiment classification models must be constantly updated and fine-tuned to adapt to these changes. Moreover, the integration of sentiment analysis with traditional quantitative methods poses difficulties, as merging qualitative sentiment data with quantitative financial metrics requires advanced analytics and domain expertise.

5. Data privacy and compliance

Compliance with regulations and keeping the data private, especially when dealing with user-generated content on social media, adds another layer of complexity to sentiment analysis in the stock market.

Enhancing trading strategies with sentiment analysis tools

Financial institutions and traders integrate sentiment analysis into their algorithmic trading strategies to gain a competitive edge. By analyzing sentiment, traders can gauge whether the market sentiment is bullish (positive), bearish (negative), or neutral. This information helps in adjusting trading strategies to capitalize on market trends and mitigate risks.

Financial institutions and traders should view sentiment analysis as a complementary tool to technical and fundamental analysis. By combining quantitative analysis with qualitative insights from sentiment analysis, traders can make more informed decisions and enhance profitability.

A special sentiment analysis model: BERT

BERT (Bidirectional Encoder Representations from Transformers) is a powerful AI model for understanding text. Developed by Google in 2018, it revolutionized how machines interpret language.

Unlike older models, it reads entire sentences at once, allowing it to understand words based on their full context. This makes it especially useful for sentiment analysis in the stock market.

Using BERT, researchers extracted sentiment from online investor discussions. They then applied attention-based weighting to calculate an investor sentiment indicator. Finally, they analyzed how sentiment influenced stock returns using a regression model.

The results showed that investor sentiment had a strong impact on stock yield. BERT achieved 97.35% accuracy, outperforming older models like LSTM and SVM. This makes it a highly effective tool for understanding market sentiment.⁸

By accurately interpreting news articles, reports, and social media, BERT leads investors to make better investments.

Further reading on sentiment analysis

• Sentiment Analysis Services Benchmarking
• Top Sentiment Analysis Tools
• Sentiment Analysis Datasets
• Top 3 MonkeyLearn Alternatives for Sentiment Analysis

External Links

• 1. Kalyani, J., Bharathi, P., & Jyothi, P. (2016). Stock trend prediction using news sentiment analysis. arXiv preprint arXiv:1607.01958.
• 2. Fombrun, C., & Shanley, M. (1990). What’s in a name? Reputation building and corporate strategy. Academy of management Journal, 33(2), 233-258.
• 3. Yan, D., Zhou, G., Zhao, X., Tian, Y., & Yang, F. (2016). Predicting stock using microblog moods. China Communications, 13(8), 244-257.
• 4. Koukaras, P., Nousi, C., & Tjortjis, C. (2022, May). Stock market prediction using microblogging sentiment analysis and machine learning. In Telecom (Vol. 3, No. 2, pp. 358-378). MDPI.
• 5. Wan, X., Yang, J., Marinov, S., Calliess, J. P., Zohren, S., & Dong, X. (2021). Sentiment correlation in financial news networks and associated market movements. Scientific reports, 11(1), 3062.
• 6. Xue, L., Wang, H., Wang, F., & Ma, H. (2021, February) Sentiment Analysis of Stock Market Investors and Its Correlation with Stock Price Using Maximum Entropy. In International Conference on Intelligence Science (pp. 29-44). Cham: Springer International Publishing.
• 7. Padmanayana, V., & Bhavya, K. (2021). Stock market prediction using Twitter sentiment analysis. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol.
• 8. Sousa, M. G., Sakiyama, K., de Souza Rodrigues, L., Moraes, P. H., Fernandes, E. R., & Matsubara, E. T. (2019, November). BERT for stock market sentiment analysis. In 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI) (pp. 1597-1601). IEEE.