The “opinio – product reviews” application provides a summary of reviews of real users from around the world.
The outputs represent the most discussed topics and features of selected products. For each feature, you can monitor positive and negative ratings, there are many options for visualization, sorting or filtering. Products can also be compared, sorted or filtered according to popularity. Statistical information is also provided – it is the discussion of brands and topics over time, as well as the sentiment related to each category statistic.
We have recently released the Smart Watch and Television categories. Both web and mobile applications can be used to select the right product based on reviews. The video shows how our applications works technically.
As we mentioned in the previous post, our team is working on a project to help you make decisions about buying different products and services. We try to help users create an objective view of the specific items they want to buy by analyzing published reviews of other users. Currently, we’ve downloaded enough reports and product articles in Czech and English language to analyze individual views. In the first phase it was necessary to adapt the obtained texts to the form suitable for analysis.
It was necessary to divide the documents into individual sentences, because users often present more ideas in one document and evaluate more criteria. The next step was to remove insignificant words that do not bring any or just little information value. For example, clutches, prepositions, web addresses, and so on. In this step, we also used our own POS analyzer, which assigns the words in sentence word types, and our own dataset with stop words. In particular, nouns, adjectives and verbs were interesting for us. Subsequently, we worded the words into their basic shapes, by specifying the roots of words.
We have transformed the edited documents into vector shape using tf-ifd and then split them into clusters with the same themes using k-means methods. We have managed to identify approximately diversified clusters with a high degree of internal integrity. Identified topics were related to the main parameters of the product segment surveyed.
The clusters created for the whole segment, based on expert articles, were then used to classify product reviews. From identified clusters for individual reviews, we chose those with the highest predictive value – and are presented as a suitable representative for a given set of reviews. The result of the analysis is shown in the example below.
In the introductory article of the Reviews section, we present the Multicriterial Text Analysis Software (MTA) project, which deals with the removal of information asymmetries in news and reviews.
The MTA team of scientists from CYRRUS ADVISORY, a.s. and Mendel University in Brno uses machine learning methods to analyze text in the field of current news and product reviews.
In the following posts, you can look forward to describing the research issues and the results we have already achieved in this area.