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If you had to sort reviews on a website and there were no ratings attached to it: how would you know if it was good or bad?

Would it be valuable for organizations to understand the public sentiment around key issues? Are consumers frustrated over an underperforming part that has slipped under the radar of management, or are they happy about a component that exceeded their expectations? From market research and public relations to developing emotional insights over internal phenomena, sentiment analysis offers a powerful understanding of how customers and stakeholders feel about specific topics.

Used for identifying emotions charged to a particular stance, sentiment analysis utilizes machine learning to consume, analyze, and label textual data with a particular feeling. The process has the capability to classify sentences with a variety of labels ranging from a simple “positive” or “negative” emotion to more complex feelings such as “anticipation,” “joy,” or “disgust.” It can also identify how strong the emotion is being portrayed, similar to a Likert Scale where some emphasis is applied to the feeling (“very positive” vs “positive.”) Strategically aligning this process on a targeted sample can allow for a comprehensive set of insights.

Out of the box, open-source tools have reasonable effectiveness, however, it carries inherent problems without competent technical support and a cohesive strategy.

What Does the Process Look Like?

Sentiment analysis works best when coupled with other machine learning techniques that work with textual data. For example, one might use an algorithm to identify and extract tweets about a particular subject. Once all of the data has been gathered it may undergo a cleaning process. Depending on the source, sometimes punctuation and capitalization must be accounted for as they both can influence the severity of the sentiment. In the case of Twitter, some users may use exclamation points sparingly while others may toss in an exclamation point with every other word. This creates an uneven playing field for accurately depicting sentiment as it depends on the source of the tweet.

Once the data has been cleaned, sentiment analysis can be applied. Depending on the dataset one might want to classify the entire body of text with a single emotion, or break down the process and label each paragraph or sentence to get a more accurate depiction of the situation. The decision is typically based on the requirements and subject matter expertise of the engineer performing the analysis.

Finally, after every data point has been analyzed, the labeled dataset will move on in the pipeline towards the final product. In some instances, the data will be passed along to another machine learning process, and in other cases, it might be stored in a database to be displayed in an application or dashboard.

How Can Sentiment Analysis Help My Business?

Sentiment analysis can be a tool for understanding how people are responding to change internally from alterations to maintenance routines, new work specifications, and many other applications. This form of analysis has large human resource potential, saving a business time and money sorting through different internal feedback reviews.

An adjacent area of application is market research, which can allow a business to understand how customers feel about a particular product or service. This information will enable businesses to make wise decisions when making changes to an existing product or developing a new one. Applying sentiment analysis to segmented population groups in key industries would save significant time developing a comprehensive understanding of an organization’s competitive advantages. Decision makers can understand the shortfalls of their competition and highlight areas of opportunity or concern. Perceptual maps can help visualize how consumers are feeling about attributes of products or services. Visualizing large samples of emotive responses towards influential problem areas and value drivers allows for irreplaceable market research.

Sentiment analysis can also be used as a rapid response tool for social media developments. Having a public relations framework to detect and identify highly controversial content can allow for social media teams to address concerns before unraveling further. It can also give PR a faster response time to address developing political, social, or industry issues, driving higher engagement, and even predicting events before they occur.

A Project From the Past

For a fun recruiting event project, the Freya Systems team developed a cookie dashboard, with the goal of determining how we could extract sentiment analysis on specific cookies (the edible kind). The team extracted Twitter data from their API and began cleaning and filtering out unnecessary items. Topic and key jargon phrase extraction was a challenge, specifically to distinguish an edible “cookie” from its other homonyms in web development for example. After cleaning out all unnecessary phrases, sentiment analysis was used to determine general opinion on cookie preference. We visualized associated word groupings onto a word cloud to show what different kinds of sentiments were accruing for each cookie type.

A key takeaway was the importance of punctuation in free text data to effectively gauge sentiment analysis. Exclamation points can add value to a sentiment so normalization of the data was key for a balanced result. We utilized Python and an NLTK package. We further developed our skills in topic modeling of large data and implementing keyphrase extraction for sentiment analysis. The Freya team has applied this innovative process to a variety of topics in aviation. When using sentiment analysis, a focused strategic objective and a competent software development support team working together can provide immensely valuable insight to decision makers.

Contact us today to learn how Freya Systems can help you use sentiment analysis to drive insights into your business.