83 – Advanced Sentiment Analysis and Reporting

Suite Spot: A Hotel Marketing Podcast

In this episode of the Suite Spot, we celebrate another exciting solution update launch with Host Ryan Embree and VP of Product & Technology, Jason Lee. Ryan and Jason roll out and explore the new advanced sentiment analysis and reporting for hotels. The episode starts with Jason defining what review sentiment is and what was behind his team’s initiative to enhance Travel Media Group’s sentiment analytics and reporting. Ryan and Jason then walk through several use cases for leveraging sentiment analysis and how hoteliers could use this data to make better operational and capital decisions for their property. They wrap up the episode talking about how this new update fits in TMG’s suite of reputation solutions and how much it will cost hotel partners. If you are interested in finding out more about Travel Media Group’s new advanced sentiment analytics or to submit a question for future episodes, call or text 407-984-7455. Suite Spot Podcast · 83 - Advanced Sentiment Analysis and Reporting Episode Transcript Our podcast is produced as an audio resource. Transcripts are generated using speech recognition software and human editing and may contain errors. Before republishing quotes, we ask that you reference the audio. Ryan Embree: Hello everyone and welcome to another episode of the Suite Spot. This is your host, Ryan Embree. Thank you for listening, wherever you are listening from out there. We've got another jam-packed, exciting episode for you today. With me today, is a very familiar guest, you've heard his voice on here and with this guest typically comes some very cool, exciting new news. Jason Lee, Vice President of Product Development and Technology at Travel Media Group. Jason, welcome back to the Suite Spot. Jason Lee: All right. Thank you, Ryan. Ryan Embree: Yeah, and we've got some exciting updates to announce on this episode and it all starts with sentiment analysis and a complete revamp and upgrade and advanced reporting for the sentiment analysis for reviews. So Jason, before we jump into these updates, I want to just start at a basic level. We've done it before, but I think it's best to reset and explain to some of our listeners, what exactly is sentiment analysis when we're talking about hotel's online reviews? Jason Lee: Okay, so when you think about sentiment, you think about what is maybe the mood of the person writing the review, right? That could be part of sentiment, like, is this person satisfied? Is this person unhappy/happy? Are they a mix of emotions, right? So there's that part of sentiment, but then there's also the other parts of it, which detail what caused that emotion. So we use something that a lot of people use, especially in machine learning or AI, which is natural language processing. So it basically takes all of this text. So you have tons and tons of text that guests have given you over time. And we take that text and then we're able to break that down into various layers. So one layer would be the sentiment of that text, which would be whether someone, whether someone was maybe generally happy or unhappy, but then we could then break it into elements. And then where there, they might be happy, neutral, or unhappy with those elements. So, so it kind of starts with natural language processing and then determining sentiment. Then it turns into clustering aspects or clustering topics. And these would be things like this happened in the room that had happened in the lobby. Did it, you know, what, where, where are we talking about? And then it, then it goes more into detail into what exactly happened in that space. So if it was something with cleanliness, what was not clean, and maybe there was more than one thing, but, but basically taking all that text and then pushing it into these like more digestible pieces of data and then, and then displaying that. So, so you can see over time if, from cleanliness, if it's, if it's something that is specific, like,