A novel deterministic approach for aspect-based opinion mining in tourism products reviews

Abstract

Articulo de publicacion SCOPUSThis work proposes an extension of Bing Liu’s aspect-based opinion mining approach in order to apply it to the tourism domain. The extension concerns with the fact that users refer differently to different kinds of products when writing reviews on the Web. Since Liu’s approach is focused on physical product reviews, it could not be directly applied to the tourism domain, which presents features that are not considered by the model. Through a detailed study of on-line tourism product reviews, we found these features and then model them in our extension, proposing the use of new and more complex NLP-based rules for the tasks of subjective and sentiment classification at the aspect-level. We also entail the task of opinion visualization and summarization and propose new methods to help users digest the vast availability of opinions in an easy manner. Our work also included the development of a generic architecture for an aspect-based opinion mining tool, which we then used to create a prototype and analyze opinions from TripAdvisor in the context of the tourism industry in Los Lagos, a Chilean administrative region also known as the Lake District. Results prove that our extension is able to perform better than Liu’s model in the tourism domain, improving both Accuracy and Recall for the tasks of subjective and sentiment classification. Particularly, the approach is very effective in determining the sentiment orientation of opinions, achieving an F-measure of 92% for the task. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions, using a non-extended approach for this task. Finally, results also showed the effectiveness of our design when applied to solving the industry’s specific issues in the Lake District, since almost 80% of the users that used our tool considered that our tool adds valuable information to their business.FONDEF, CONICY

Towards Making Sense of Online Reviews Based on Statement Extraction

“…The sentiment is given by an orientation and not the actual information like done here. The same underlying method was later also used and enhanced by Marrese-Taylor et al [18] where they conducted a user study with a visual overview over the sentiment for each aspect.…”

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

“…When the document-or sentence-based sentiment analysis is employed, it is not easy to justify which topic ("product" or "service") the sentiments ("good" or "bad") belong to. Hence, many researchers have begun to conduct topic-based sentiment analyses [10][11][12]14].…”

“…Existing polarity classification methods at the topic level [10,13,14] have focused on using entire reviews. Instead of using entire reviews, we propose to use the reviews related to one or two topics.…”

“…The topic-based sentiment analysis approach was also applied to extract tourism-related topics (e.g., hotel services and prices) using online reviews [14][15][16]. Previous topic-based sentiment analysis studies fall into two categories: topic extraction using topic modeling [14][15][16][17] and polarity classification at the topic level [12,13]. Topic extraction is the process of extracting topics that have been evaluated, while polarity classification at the topic level classifies the sentiments in each topic into positive, negative, or neutral [18].…”

Opinion Score Mining: An Algorithmic Approach

“…The polarity scores are shown with the words in Princeton WordNet in technical terms [16]. They particularly assign polarity scores to each Sysnet in the Wordnet [17]. Use of dictionary will help in finding synonyms and antonyms relations of the words which are not present in the Opinion lexicon for determining the polarity of new opinion words.…”