In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the cooccurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.

We present, in this paper, our contribution in SemEval2017 task 4 : "Sentiment Analysis in Twitter", subtask A: "Message Polarity Classification", for English and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman's (2003) seed words, on polarity classification of tweet messages.

Book search is a challenging task due to discrepancies between the content and description of books, on one side, and the ways in which people query for books, on the other. However, online reviewers provide an opinionated description of the book, with alternative features that describe the emotional and experiential aspects of the book. Therefore, locating emotional sentences within reviews, could provide a rich alternative source of evidence to help improve book recommendations. Specifically, sentiment analysis (SA) could be employed to identify salient emotional terms, which could then be used for query expansion? This paper explores the employment of SA based query expansion, in the book search domain. We introduce a sentiment-oriented method for the selection of sentences from the reviews of top rated book. From these sentences, we extract the terms to be employed in the query formulation. The sentence selection process is based on a semi-supervised SA method, which makes use of adapted word embeddings and lexicon seed-words. Using the CLEF 2016 Social Book Search (SBS) Suggestion Track Collection, an exploratory comparison between standard pseudorelevance feedback and the proposed sentiment-based approach is performed. The experiments show that the proposed approach obtains 24%-57% improvement over the baselines, whilst the classic technique actually degrades the performance by 14%-51%.

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This paper briefly describes our research groups' efforts in tackling Task 1 (Early Detection of Signs of Self-Harm), and Task 2 (Measuring the Severity of the Signs of Depression) from the CLEF eRisk Track. Core to how we approached these problems was the use of BERT-based classifiers which were trained specifically for each task. Our results on both tasks indicate that this approach delivers high performance across a series of measures, particularly for Task 1, where our submissions obtained the best performance for precision, F1, latencyweighted F1 and ERDE at 5 and 50. This work suggests that BERTbased classifiers, when trained appropriately, can accurately infer which social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and early detection scoring mechanism, as well as apply the same approach to other related tasks (e.g., anorexia, depression, suicide).