In the first category, the paper ‘Movie Rating Prediction using Sentiment Features’, by Apóstolo et al., describes a new sentiment lexicon, Expanded OntoSenticNet (EON), which combines OntoSenticNet with SentiWordNet and is used to predict movie ratings. Likewise, ‘Sentiment Analysis of Serbian Old Novels’, by Stanković et al., describes a polarity lexicon in Serbian based on three existing lexicons (NRC, Affin, and Bing), the Ontolex-lemon model, and the sentiment vocabulary Marl. Finally, ‘Evaluating a New Danish Sentiment Resource: the Danish Sentiment Lexicon, DSL’, by Schneidermann and Pedersen, describes an evaluation of Danish sentiment lexicons. We are pleased to highlight the importance of publishing non-English sentiment lexicons for resource-scarce languages.

The second category has been addressed in the paper ‘O-Dang! The Ontology of Dangerous Speech Messages’, by Stranisci et al., which defines an interoperable knowledge graph to link linguistic resources related to dangerous speech. The graph was populated with eight Italian language resources on dangerous speech, demonstrating the considerable potential of linked data technology to interlink language resources.

Finally, the use of open-linked data for sentiment analysis is exploited in ‘Correlating Facts and Social Media Trends on Environmental Quantities Leveraging Commonsense Reasoning and Human Sentiments’, by McNamee et al. This paper analyzed how external information (e.g. temperature or pollution) can be combined with the insights of sentiment analysis.

While SALLD is gradually evolving into a platform where different communities (computer science, linguistics, information science, knowledge engineering, language data science) interact and cooperate within the scope of SA and LLD, we note that the interoperability of these two factors is still rare in most of the submissions we receive. It is hoped that future editions of this workshop will contribute to further strengthening the relevant collaboration between these two fields.

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