Online retailers and businesses use text mining (opinion and sentiment analysis) for understanding customers’ needs and improve customer service. In the e-commerce industry using sentiment analysis tools can help them to become closer to the key stakeholders. Nowadays because of social media in the e-commerce industry, there is competition. Every product, brand, and the place has many reviews, so it even difficult for the customer making a decision as well as businesses to manage customer opinions. Therefore, operation and classifies words by sentiment analysis help e-commerce improve business and help customers to choose a better option.
Obviously working with text could be more difficult because it is not just about the data and needs time to prepare the dataset and then get the result and analysis results to take time to understand the problem. Maldives hotel reviews dataset has 8 attributes and 21071 instances. In this dataset, using reviews of 106 hotels in the Maldives. I used column reviews and hotel names for sentiment analysis, I used the lexicon method to count the number of positive and negative words for each hotel.
variables name and type in the Maldives hotel reviews dataset (21071 instances)
|Variable name||Data type||Description|
|Review id||Nominal||The unique ID: include 9-digit integral number with two letters|
|Hotel Name||Nominal||Hotel name in the Maldives|
|Total review count||Numeric||Total number of reviews for each hotel|
|Review_viaMobile||Nominal||Customer use of mobile for review or not|
|Review Date||Numeric||Review Date: the year and date of reviews for each hotel|
|Guest Location||Nominal||Country name and city for each customer|
|Review Heading||Text||Title of review|
|Review||Text||Text review for each hotel|