A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter

[EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170.The work reported in this paper is supported by a grant from the project “CLIA System Phase II” funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030).Gopal Patra, B.; Mazumda, S.; Das, D.; Rosso, P.; Bandyopadhyay, S. (2018). A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter. 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A novel NIH research grant recommender using BERT.

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the system's usefulness. During internal evaluations, the grant citations were used to establish grant-publication ground truth, and results were evaluated against Recall@k, Precision@k, Mean reciprocal rank (MRR) and Area under the Receiver Operating Characteristic curve (ROC-AUC). During external evaluations, researchers' publications were clustered using Dirichlet Process Mixture Model (DPMM), recommended grants by our model were then aggregated per cluster through Recency Weight, and finally researchers were invited to provide ratings to recommendations to calculate Precision@k. For comparison, baseline recommenders using Okapi Best Matching (BM25), Term-Frequency Inverse Document Frequency (TF-IDF), doc2vec, and Naïve Bayes (NB) were also developed. Both internal and external evaluations (all metrics) revealed favorable performances of our proposed BERT-based recommender

Relations between publications and RFAs through project number.

Relations between publications and RFAs through project number.</p

Test results for baselines vs. our proposed method.

Test results for baselines vs. our proposed method.</p

Train, validation and test data.

Train, validation and test data.</p