Mining the Precious Insights in Unstructured Data With Sentiment Analysis

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Data Society         
machine learning
October 2022             
machine learning         Blog
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On their journey toward data transformation, many organizations have come a long way in a short time. However, a vast data frontier remains relatively unexplored. A voluminous supply of unstructured data proliferates through sources such as video, audio, and social media posts. However, although this mass of qualitative data is estimated to represent 80 to 90 percent of all data, 90 percent of it is never analyzed. That’s a breathtaking quantity of valuable insights remaining undiscovered. Yet, organizations equipped to perform sentiment analysis can tap this rich reservoir of insights.

Dredging Oceans of Text Data to Capture Sentiments

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Sources of unstructured data can be found everywhere, including product reviews, call center logs, virtual meeting recordings, email, social media posts, reviews, and chatbot records. Through these documents, organizations can perform analytics that leads to informed strategies to pursue a wide range of goals, such as:

  • Enhancing customer experience.
  • Anticipating market demands.
  • Identifying reputational weaknesses.
  • Improving product design.
  • Evaluating competition. 
  • Gauging workforce contentment
  • Detecting consumer dissatisfaction.


Sentiment analysis, also known as opinion mining, uses Natural Language Processing (NLP) and Machine Learning (ML) to extract the public’s feelings and viewpoints in these unstructured data sources. Unlike conventional surveys, sentiment analysis eliminates the time-consuming efforts involved in reading through copious text data, enabling analysts to mine large data sets for valuable insights in minimal time. In addition, by sourcing information from free-style text documents, sentiment analysis often reveals more authentic and nuanced feedback and can illuminate patterns in the appearance of specific words. Thus equipped, analysts can also quantify sentiments by scoring text content as positive, negative, or neutral or on a continuum from strongly negative to strongly positive.

Sentiment Analysis at Work in Public Health

Sentiment analysis is a versatile tool that can deliver critical insights for application in both the private and public sectors. An example of sentiment analysis deployed to address public needs is a solution Data Society developed for a communications firm responsible for a state department of health’s COVID-19 vaccination messaging campaign. As part of this project, the Data Society team used advanced data science techniques to give further insights into campaign engagements where more data was available. For example, the Data Society team could display sentiment analysis per the social media posts on varying keyword clusters associated with vaccine distribution. This information helped identify which message types resonated most with audiences, along with heat maps showing the location of users’ posts and the number of posts about vaccination over time. Through this analysis, the client demonstrated the impact of their communications work and tailored ongoing engagements to increase vaccinations. 

Sentiment Analysis Challenges and Solutions

Despite sentiment analysis’s many benefits, interpreting text through machine learning algorithms is tricky. For example, detecting words correlated with negative or positive sentiment, such as “good” or “bad,” doesn’t necessarily capture the attitude behind the message, and some words can be either positive or negative depending on the context. Also, slang, misspellings, and rhetorical devices such as sarcasm can be difficult for sentiment analysis tools to classify correctly on a favorability scale. 

For this reason, many effective approaches to sentiment scoring rely on deep learning to train algorithms that take context and variations in the lexicon into account. Although trained algorithms can also derive meaningful analysis specific to a domain, these tools can be tailored to capture the sentiments most relevant to the organization's offerings and goals. Sentiment scoring tools can also minimize erroneous scoring in cases where conflicting sentiments appear in the same sentence by parsing the text into clauses, identifying the different aspects they contain, and assigning separate ratings to each aspect. In addition, sentiment scoring tools can assign varying weights to shared aspects, depending on the types of insights the organization is seeking and how they will be applied. 

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This information can help organizations gain critical perspectives on trends impacting a range of industries, revealing insights into such essential business concerns as:

  • Brand reputation.
  • Competition.
  • Customer satisfaction.
  • Consumer preferences.
  • Product quality. 
  • Employee perspectives.

By performing sentiment analysis, organizations can develop a more timely and illustrative picture of public perception on a broad range of significant issues. Business functions that can benefit from sentiment analysis include: 

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Marketing - Marketers can use sentiment analysis tools to gauge consumer intent, enabling them to target advertising and customize messaging to appeal to an audience most likely to purchase a product or service. These tools also help marketers assess the impact of marketing campaigns.

Sales - With sentiment scoring tools, sales teams can quickly identify and qualify sales leads. In addition, sentiment analysis can offer an effective approach to market research, enabling sales teams to glean insights into the needs and preferences of potential customers and tailor their sales calls accordingly.

Product Development - Sentiment analysis can help businesses track consumer responses to specific elements of products, informing strategies for improving their offerings. 

Customer Service - Businesses can leverage sentiment analysis tools to monitor sources such as chatbot interactions, call logs, social media posts, customer email, and online reviews to detect, understand, and take informed action to address customer dissatisfaction. 

HR - HR departments can apply sentiment analysis tools to employee feedback to better understand workforce engagement, anticipate workforce trends, and gain insights into how employees feel about their workplace and jobs. In addition, assessing workforce sentiments can also inform organizational DEI initiatives.

Market Forecasting - In the financial services industry, institutions can leverage sentiment analysis of financial news to perform predictive analytics pertaining to stock market trends.

Unlocking Unstructured Data’s Value for Your Organization

The business value of sentiment analysis is evident. However, to reap the full benefits of unstructured data sources using sentiment analysis tools, workforces must be equipped with data literacy. With data science training, organizations can upskill their teams and empower them to mine the wealth of insights into feelings, opinions, and emotions that sentiment analysis can reveal.  


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Data Society provides customized, industry-tailored data science training solutions—partnering with organizations to educate, equip, and empower their workforce with the skills to achieve their goals and expand their impact.

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