Your hotel deals with thousands of reviews from TripAdvisor, Yelp and your own satisfaction surveys. Nevertheless, nobody can process all this information and get accurate insights of what is going on. That's why, companies start relying on machines to do this task automatically by using sentiment analysis tools.
The hospitality industry is receiving vast amount of customer feedback data everyday. Hotels and restaurants are always striving to improve their brand performance and customer loyalty to fight against competitors. Consequently, they collect all the customer reviews posted in online platforms, social media, and even, in their own company surveys. Why are they doing that? Just to see what their customers think about them.
Reviews are a great source of opinions that help offer better products and services. The big problem comes when separating the wheat from the chaff to get relevant insights for your business from all customer reviews complied - and not die trying.
Sentiment Analysis to get automatically feedback data insights
It's possible that you already heard something about sentiment analysis and know what the current technology allows. These days, there are several tools available to seek from a vast collection of texts to few insights relevant for business decisions. Thanks to the most recent advances in NLP and computational power, recognizing the reputation any global brand has is almost an automated process.
What kind of business insights sentiment analysis provides?
Analyzing all the feedback your customers write, companies can recognize some business insights like:
- What particular hotel is having bad reputation
- Detect what specific services (food providers, room services, laundry, …) are disappointing guests
- What areas are working better, e.g. satisfaction with lunch menu
These insights allow your company to take measures for an improvement in areas of dissatisfaction. Additionally, it will let you know new initiatives to attract more customers.
How does sentiment analysis work?
There are some sentiment analysis tools in the market, but no all of them are equally accurate. While some may be based just on keywords or syntactic tagging, there are others, as the ones from Bitext, based on text analytics and NLP resources enriched with linguistic knowledge. This second approach extract in detail what customers feel about particular topics and how strong those feelings are. This is how sentiment analysis process should look like:
- Firstly, the review undergoes segmentation and tokenization: this is as simple as splitting an answer into sentences and then into words, following the same hierarchy we used when reading.
- Then, each element is tagged: using lexicons, words or expressions tagged with their equivalent attributes (POS, morphological attributes together with functional and/or semantic attributes).
- Once tagging is done, your sentiment analysis tool conduct a syntactic analysis to correctly identify the topic a customer is talking about. In reviews like “The restaurant on the beach was awful”, this tool identify that the feedback as a whole refers to restaurant, and not to beach, understanding the hierarchy relationships between words.
- A last part is the semantic analysis: the platform makes use of a set of semantic dictionaries, tailor-made for specific needs and different domains. What's more, these dictionaries can be modified and adapted by the user without further assistance, thus receiving full power over their results.
Thanks to our NLP approach for sentiment analysis, Bitext can analyze your reviews correctly while other tools fail. Here you have some examples:
- Correct selection of topics:
Took my twins for their birthday, and the food was awful.
Correct topic: 'food'; Incorrect topic identification 'twins', 'birthday'.
- Diverse topics and sentiments associated to them in one sentence.
All the starts and appetizers were incredible and the lasagna was mouth-watering.
'Incredible' refers to starts and appetizer and 'mouth-waterin' refers to lasagna.
- Correct sentiment assignment based on context
'High quality' (as positive) vs 'High price' (negative)
- Correct identification of sentiment expressions (not just words):
The service I received from ACME restaurant was beyond the call of duty. (positive)
Waiters services were by no means the worst ones I have ever seen. (negative)
The hotel spa services were second to none. (positive)
If you want to have a look at our sentiment analysis tool, register now on our API platform, and go to Sentiment Analysis menu option. There you could try how it works by giving a review sample to be analyzed. Easy as pie.