How to Perform Sentiment Analysis on Reviews

Title: How To Perform Sentiment Analysis on Reviews

How does your customer feel about your product or service? That’s a vital question business shouldn’t ignore. 

Whether positive or negative words matter. They can boost a business or initiate a crisis. But, the good news is that you can measure customer satisfaction through sentiment analysis. 

Sentiment analysis uses Machine learning to classify emotions in a text. For businesses, it’s a chance to learn how customers feel about a brand, product, or service. Especially in the travel industry, online reviews hold a trove of insights that can potentially bring improvements. After all, you must stand out in the saturated market and craft unforgettable experiences by listening attentively to customer feedback. 

Considering TripAdvisor alone, there are around 702 million reviews of leading hotels worldwide. But where to start that without investing too many manual hours?

Sentiment analysis tools come to your rescue. They use Machine Learning to automate the process, so you can surf through thousands of reviews in minutes.   

Let’s learn how to perform sentiment analysis on many TripAdvisor reviews.

How To Analyze Sentiment On TripAdvisor Reviews?

Through machine learning tools, let’s analyze a set of hotel reviews from TripAdvisor. Precisely the core goal is to classify each opinion as positive, negative, or neutral. 

Here are five steps to help you build a sentiment analysis and visualize data. Let’s get started! 

#Step 1: Gather TripAdvisor Reviews 

First and foremost, collect your data. In this case, a TripAdvisor review collection is saved in an Excel or a CSV file. Besides, you can automate the data collecting process using web scraping software. 

A handful of visual scraping tools are: 

Dexi.io  - It’s a simple point-and-click interface to collect data from different online sources. 

Import.io - It’s a powerful web scraping solution to extract data on a big scale. You can easily click on the data you would like to extract, create a sequence of actions to perform on a website, or set up an automation to capture data frequently.  

ParseHub - This is a free, easy-to-use web scraper tool. You just need to click on the type of data you want to extract. Then, you can easily integrate your data with other apps using an API.  

#Step 2: Clean Your Data

The data scraped from websites often contains a lot of noise - errors, meaningless information, inconsistent formatting, or incomplete sentences. This eventually makes it daunting for machines to process and then affects the results of your analysis. 

To avoid this, you’ll need to pre-process or clean your data before performing any text analysis. Here are some approaches by which you can ultimately prepare your data and improve its quality. 

• Correct spelling mistakes and write full words rather than using abbreviations or acronyms. 

• Remove stop words, like a, at, from, and there, that frequently appear in texts but don’t add any relevant information. 

• Minimize words to their root form

• Convert all your text data to lowercase. 

#Step 3: Classify Your Data Into Opinions Units

When you read hotel reviews on Tripadvisor, you’ll observe that each review consists of different statements. Too frequently, a customer praises one aspect of a hotel but criticizes others. 

But, you can accurately split all the opinions in your TripAdvisor dataset by simply uploading your file as a batch into your sentiment analysis tool. 

#Step 4: Create a Sentiment Analysis Model

A reliable sentiment analysis tool provides various text analysis tools to find topics, sentiment, keywords, and much more in voluminous data. 

The easiest way to start with sentiment analysis is by using pre-trained models that are ready to use and perform well in everyday use cases. 

So, choose a trusted no-code platform if you want to create your sentiment analysis model personalized to your TripAdvisor dataset. Then, create customized models for sentiment analysis, and train them directly in the interface. 

#Step 5: Use Your Sentiment Analysis Model

To analyze your TripAdvisor dataset with your custom sentiment analysis model, select one of the three options:

• Integrations - Connect your model to your favourite apps like Zendesk, Gmail, Repustate, etc. This proves to be beneficial in analyzing other types of data like customer support tickets, emails or survey responses. 

• Batch processing - In this, you upload your Tripadvisor reviews as a CSV or an Excel file. Then, your sentiment analysis tool processes the batch and returns an Excel file with all the opinions tagged as Positive, Negative or Neutral along with a confidence score. 

• API - Manage and run your sentiment analysis model using your preferred programming language. Usually, the API response will be in JSON format. 

Visualize Sentiment in TripAdvisor Review 

Once you have classified all your reviews by sentiment, it’s time to visualize results to ensure your data is engaging and easy to understand. Data visualisation allows you to identify insights and trends that may not be evident in an Excel file. 

Word clouds are considered the most straightforward way of visualizing sentiment data. In this, you can create a word cloud to discover which word appears more frequently on positive, negative, and neutral reviews. 

To do so, you have to filter opinions by sentiment in your Excel file, copy all the views from a specific category, and paste them into your tool's free word cloud generator. 

Finally

Travellers often scroll Tripadvisor as a source of truth, so it’s imperative to stay on top of your hotel reviews to keep negative comments at bay. Sentiment analysis quickly tells you what your customers love or hate about their experience and helps you uncover opportunities to improve your business. 

In short, do you want to grow your revenue by 12%? Sentiment analysis is the hack to do it!