Optimal Strategies to Perform Multilingual Analysis of Social Content
  for a Novel Dataset in the Tourism Domain
Maxime Masson Rodrigo Agerri Christian Sallaberry Marie-Noelle Bessagnet Annig Le Parc Lacayrelle Philippe Roose
The rising influence of social media platforms in various domains, including
tourism, has highlighted the growing need for efficient and automated natural
language processing (NLP) approaches to take advantage of this valuable
resource. However, the transformation of multilingual, unstructured, and
informal texts into structured knowledge often poses significant challenges.
  In this work, we evaluate and compare few-shot, pattern-exploiting and
fine-tuning machine learning techniques on large multilingual language models
(LLMs) to establish the best strategy to address the lack of annotated data for
3 common NLP tasks in the tourism domain: (1) Sentiment Analysis, (2) Named
Entity Recognition, and (3) Fine-grained Thematic Concept Extraction (linked to
a semantic resource). Furthermore, we aim to ascertain the quantity of
annotated examples required to achieve good performance in those 3 tasks,
addressing a common challenge encountered by NLP researchers in the
construction of domain-specific datasets.
  Extensive experimentation on a newly collected and annotated multilingual
(French, English, and Spanish) dataset composed of tourism-related tweets shows
that current few-shot learning techniques allow us to obtain competitive
results for all three tasks with very little annotation data: 5 tweets per
label (15 in total) for Sentiment Analysis, 10% of the tweets for location
detection (around 160) and 13% (200 approx.) of the tweets annotated with
thematic concepts, a highly fine-grained sequence labeling task based on an
inventory of 315 classes.
  This comparative analysis, grounded in a novel dataset, paves the way for
applying NLP to new domain-specific applications, reducing the need for manual
annotations and circumventing the complexities of rule-based, ad hoc solutions.