An interactive sentiment map generated by analyzing tens of thousands of hotel guest reviews has been posted online by Olset, a hotel data specialist, which scrapes the data from multiple sources, such as Booking.com, The map, limited to the US, shows sentiments about more than 40 features mentioned in hotel reviews. It shows the most-positive and most-negative things from an aggregate state-level all the way to individual properties. For instance, in California people complain about staff much more than they do in Colorado. Say you're a traveler who cares in particular about the air conditioning at a hotel.
In my previous blog post I wrote about using BeautifulSoup for scraping over two thousand Flixbus customer reviews and identifying company's strengths and weaknesses by performing NLP analysis. Building up on previous story, I decided to use the collected text data to train a Recurrent Neural Network model for predicting customers' sentiment, which proved to be highly efficient scoring 95.93% accuracy on the test set. Without any further ado let's jump into implementation. As a starting point, I loaded a csv file containing 1,780 customer reviews in English with the corresponding rating on the scale from 1 to 5, where 1 is the lowest (negative) and 5 is the highest (positive) rating. Now we have the data to work with.
If a person wishes to relax himself, travelling is probably the best pick for most people. Choosing the right place to stay for your vocation is one of the most important parts in a travel, but how to do so may be a problem. Reading through reviews of a certain hotel may be a good choice, referring to visitors' experience, you get to know some more specific details about the hotel, however, this method is not comprehensive enough, and reading a bunch of reviews would irritate you. Here is a way I would like to introduce to y'all, easy, fast and accurate, conducting a sentiment analysis. Sentiment analysis, also known as opinion mining, is the process of computationally identify and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc.
We explore the relationship between negated text and negative sentiment in the task of sentiment classification. We propose a novel adjustment factor based on negation occurrences as a proxy for negative sentiment that can be applied to lexicon-based classifiers equipped with a negation detection pre-processing step. We performed an experiment on a multi-domain customer reviews dataset obtaining accuracy improvements over a baseline, and we further improved our results using out-of-domain data to calibrate the adjustment factor. We see future work possibilities in exploring negation detection refinements, and expanding the experiment to a broader spectrum of opinionated discourse, beyond that of customer reviews.
One of the most frequently cited sarcasm realizations is the use of positive sentiment within negative context. We propose a novel approach towards modeling a sentiment context of a document via the sequence of sentiment labels assigned to its sentences. We demonstrate that the sentiment flow shifts (from negative to positive and from positive to negative) can be used as reliable classification features for the task of sarcasm detection. Our classifier achieves the F 1 -measure of 0.7 for all reviews, going up to 0.9 for the reviews with high star ratings (positive reviews), which are the reviews that are materially affected by the presence of sarcasm in the text.