Abstract-Web intelligence applications track online sources with economic relevance such as customer reviews, news articles and social media postings. Automated sentiment analysis based on lexical methods or machine learning identifies the polarity of opinions expressed in these sources to assess how stakeholders perceive a topic. This paper introduces a hybrid approach that combines the throughput of lexical analysis with the flexibility of machine learning to resolve ambiguity and consider the context of sentiment terms. The context-aware method identifies ambiguous terms that vary in polarity depending on the context and stores them in contextualized sentiment lexicons. In conjunction with semantic knowledge bases, these lexicons help ground ambiguous sentiment terms to concepts that correspond to their polarity. This grounding paves the way for interlinking, extending, or even replacing contextualized sentiment lexicons with semantic knowledge bases. An extensive evaluation applies the method to user reviews across three domains (movies, products and hotels).

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

Facebook is a popular social media platform used by both the demand and the supply sides of the tourism industry. Since there is a huge amount of information on the Internet, which can lead to information overload, individuals tend to apply the principle of least effort in attempting to obtain useful information as quickly and easily as possible. One of the easiest ways to retrieve travel information is by visiting the Facebook pages of destinations. This study investigates the foundations of the usefulness of Facebook Statistics: in particular of likes on DMO Facebook pages as a potential predictor of tourism demand, in addition to previous arrival numbers. In- and out-of-sample results show that the DMOs of Graz, Innsbruck, Salzburg, and Vienna can already utilize likes as an expedient leading indicator for demand, albeit not the only one. These findings are recommended to be incorporated into the DMOs’ marketing efforts.

Using data for the period 2010M06–2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organizations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error–trend–seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.

No abstract