This article presents a study conducted at the University of Málaga with the participation of second-year students from the Degree in English Studies. It focuses on a Tik Tok project that the participants had to edit for the British History class in the academic year 2020/2021. The students’ reception of said project as an innovative learning tool, both as applied to English as a second language and to the content of the courses, was analysed and measured using a questionnaire that was elaborated ad hoc and properly validated. Our results indicate great success and acceptance of the activity on the part of the students, who consider that this innovative approach to learning being highly integrated with new technologies fosters the comprehension and active learning of the subject, thus enhancing comprehension in a stimulating and motivating way. Key words: Tik Tok, learning tool, innovative approach, new technologies, motivation.

This article attempts to study the language of happiness from a double perspective. First, the impact and relevance of sentiment words and expressions in self-reported descriptions of happiness are examined. Second, the sources of happiness that are mentioned in such descriptions are identified. A large sample of “happy moments” from the HappyDB corpus is processed employing advanced text analytics techniques. The sentiment analysis results reveal that positive lexical items have a limited role in the description of happy moments. For the second objective, unsupervised machine learning algorithms are used to extract and cluster keywords and manually label the resulting semantic classes. Results indicate that these classes, linguistically materialized in compact lexical families, accurately describe the sources of happiness, a result that is reinforced by our named entities analysis, which also reveals the important role that commercial products and services play as a source of happiness. Thus, this study attempts to provide methodological underpinnings for the automatic processing of self-reported happy moments, and contributes to a better understanding of the linguistic expression of happiness, with interdisciplinary implications for fields such as affective content analysis, sentiment analysis, and cultural, social and behavioural studies.

In the context of the COVID-19 pandemic, social media platforms such as Twitter have been of great importance for users to exchange news, ideas, and perceptions. Researchers from fields such as discourse analysis and the social sciences have resorted to this content to explore public opinion and stance on this topic, and they have tried to gather information through the compilation of large-scale corpora. However, the size of such corpora is both an advantage and a drawback, as simple text retrieval techniques and tools may prove to be impractical or altogether incapable of handling such masses of data. This study provides methodological and practical cues on how to manage the contents of a large-scale social media corpus such as Chen et al. (JMIR Public Health Surveill 6(2):e19273, 2020) COVID-19 corpus. We compare and evaluate, in terms of efficiency and efficacy, available methods to handle such a large corpus. First, we compare different sample sizes to assess whether it is possible to achieve similar results despite the size difference and evaluate sampling methods following a specific data management approach to storing the original corpus. Second, we examine two keyword extraction methodologies commonly used to obtain a compact representation of the main subject and topics of a text: the traditional method used in corpus linguistics, which compares word frequencies using a reference corpus, and graph-based techniques as developed in Natural Language Processing tasks. The methods and strategies discussed in this study enable valuable quantitative and qualitative analyses of an otherwise intractable mass of social media data.

Sentiment analysis is a natural language processing task that has received increased attention in the last decade due to the vast amount of opinionated data on social media platforms such as Twitter. Although the methodologies employed have grown in number and sophistication, analysing irony and sarcasm still poses a severe problem. From the linguistic perspective, sarcasm has been studied in discourse analysis from several perspectives, but little attention has been given to specific metrics that measure its relevance. In this paper we describe the creation of a manually-annotated dataset where detailed text markers are included. This dataset is a sample from a larger corpus of tweets (n= 76,764) on two highly controversial films: Cats and Star Wars: The Rise of Skywalker. We took two different samples for each film, one before and one after their release, to compare reception and presence of sarcasm. We then used a sentiment analysis tool to measure the impact of sarcasm in polarity detection and then manually classified the mechanisms of sarcasm generation. The resulting corpus will be useful for machine learning approaches to sarcasm detection as well as discourse analysis studies on irony and sarcasm.