Multifamily owners and operators with extensive community portfolios must be able to keep a pulse on what’s happening and what’s being said about every property, which can be difficult. Sentiment analysis may be the solution you need to keep eyes and ears on what’s happening at each. This technology alerts you to problems happening in your communities, which allows you to quickly identify and rectify the issue. Monitoring what’s being said about your communities through sentiment analysis and acting on the findings shows residents and prospects that you are listening and have their best interest in mind.

What is Sentiment Analysis?

Marketers gain a better understanding of the opinions, emotions, and attitudes of customers by using sentiment analysis to determine the emotional tone behind a series of words. This tool can be useful in review monitoring to provide you with insight into the wider public opinion of your properties. Sentiment analysis tools have the ability to extract insights from social media data and online reviews to gauge the opinions and emotions of your residents. Using AI, you can understand the emotion behind what’s being said and drive real operational impact.

How it Works: Classifying Sentiment

Sentiment analysis classifies common text and determines whether the underlying sentiment is positive, negative, or neutral. Machine learning techniques and the field of natural language processing play a role in sentiment analysis. G5 Reputation and Social uses a tool called Pulse to identify keywords within each social post or review and assigns a sentiment score to the specific terms.

Find Where Your Problems Lie

Sentiment analysis helps alert you to problems that may arise within your communities. Pulse uses AI to score the sentiments of social posts and reviews, finding the issues that arise in apartment communities. Analyzing these conversations gives you an idea about your overall brand perceptions as well as specific problems within your communities. For example, you may find that your leasing staff continually receives overall positive sentiment in your analysis, which shows that your team performs at a high level and residents connect with them. Through sentiment analysis, you may discover that security, cleanliness, or a lack of parking may be concerns for your residents.

Digging deeper into the emotion and intent of specific comments can alert you to problems in certain communities. Being able to identify and rectify these issues helps increase the operational impact of your communities, leading to higher customer satisfaction and decreased turnover in apartment rentals. This advanced technology helps your communities on the ground by analyzing social media data to get meaningful and actionable operational insights.

Using sentiment analysis in your social media monitoring helps you prioritize action, showing you which fires to put out first. You can also use sentiment analysis to track trends and conversations over time. After you’ve resolved problems at your own communities, you can perform sentiment analysis on your competition the same way you track your own. This allows you to swoop in and show potential leads how you close the gap on problems at your competition’s properties.

Keep a Pulse on Your Communities with Sentiment Analysis

Keeping a pulse on all of your communities and affecting positive change on an operational level is a core capability of sentiment analysis. Analyze social posts, blogs, news articles, and other text from the Internet over a period of time to see the sentiment of a particular audience. It allows you to categorize the urgency of all digital mentions of your brand and gives you a better understanding of your presence online. Machine learning helps the technology evolve, alerting you to potential problems in your communities and understanding the sentiment behind every online post.

Do you want to keep a pulse on all of the communities in your multifamily portfolio? Schedule a demo of G5 Social and Reputation Management and learn more about sentiment analysis.